Isolation and Quantification of Metabolite Levels in Murine Tumor Interstitial Fluid by LC/MS    

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Original research article

A brief version of this protocol appeared in:
Apr 2019

 

Abstract

Cancer is a disease characterized by altered metabolism, and there has been renewed interest in understanding the metabolism of tumors. Even though nutrient availability is a critical determinant of tumor metabolism, there has been little systematic study of the nutrients directly available to cancer cells in the tumor microenvironment. Previous work characterizing the metabolites present in the tumor interstitial fluid has been restricted to the measurement of a small number of nutrients such as glucose and lactate in a limited number of samples. Here we adapt a centrifugation-based method of tumor interstitial fluid isolation readily applicable to a number of sample types and a mass spectrometry-based method for the absolute quantitation of many metabolites in interstitial fluid samples. In this method, tumor interstitial fluid (TIF) is analyzed by liquid chromatography-mass spectrometry (LC/MS) using both isotope dilution and external standard calibration to derive absolute concentrations of targeted metabolites present in interstitial fluid. The use of isotope dilution allows for accurate absolute quantitation of metabolites, as other methods of quantitation are inadequate for determining nutrient concentrations in biological fluids due to matrix effects that alter the apparent concentration of metabolites depending on the composition of the fluid in which they are contained. This method therefore can be applied to measure the absolute concentrations of many metabolites in interstitial fluid from diverse tumor types, as well as most other biological fluids, allowing for characterization of nutrient levels in the microenvironment of solid tumors.

Keywords: Nutrients, Metabolomics, Microenvironment, Interstitial fluid, Cancer metabolism, Mass spectrometry

Background

Cell division requires the duplication of the biomass of the mother cell prior to division. As a result, growing cells must be able to utilize the nutrients available in their environments to synthesize the macromolecules required to divide. To sustain cancerous proliferation, tumors often exhibit altered metabolism (DeBerardinis and Chandel, 2016). In many cases, tumor metabolism is driven by cell-intrinsic processes such as oncogenic activation (Cairns et al., 2011; Nagarajan et al., 2016). However, recent work has highlighted the importance of cell-extrinsic factors in dictating cancer cell metabolism (Anastasiou, 2017; Bi et al., 2018; Muir et al., 2018). The importance of the extracellular environment in shaping cancer metabolism is perhaps unsurprising, as the nutrient environment in which a cancer cell exists constrains which metabolic reactions are possible within that cell. Since cell-extrinsic metabolite levels can play a role in determining the behavior of tumor cells, it is critical to examine tumor cell metabolism under physiological conditions. However, our understanding of the metabolic composition of the tumor microenvironment is lacking.

The nutrient environment that a cancer cell has access to is predominantly composed of interstitial fluid (Wiig and Swartz, 2012). Understanding the nutrient content of tumor interstitial fluid would provide insight into the metabolic constraints imposed upon tumor cells by their environment. There exist multiple methodologies for isolating interstitial fluid from normal organs and from tumors (Wiig et al., 2010). However, early attempts to measure the nutrient content of interstitial fluid were limited by their inability to measure multiple metabolites, and consequently our knowledge of nutrient availability in tumors is restricted to a few metabolites in a limited number of animal tumor models (Burgess and Sylven, 1962; Gullino et al., 1964). The advent of mass spectrometry has allowed for detection of many metabolites simultaneously. However, despite technological advances, metabolomics studies are complicated by the fact that components present in biological fluids can suppress or enhance the detection of specific metabolites. These discrepancies in detection of metabolites between different biological fluids are termed “matrix effects,” and are a major confounding factor in comparing metabolite concentrations between different biological fluids and in accurately quantitating metabolites in those fluids (Panuwet et al., 2016; Sullivan et al., 2019).

Here we demonstrate a method for centrifugation-based isolation of tumor interstitial fluid and the subsequent absolute quantitation of numerous metabolites within that fluid using stable isotope dilution, a technique in which stable isotope-labeled internal standards for metabolites of interest are added to experimental samples. These stable isotope internal standards are subject to the same matrix effects as the corresponding metabolite in the sample and can be distinguished by their increased mass compared to the metabolites in the sample. To measure many metabolites simultaneously, we first quantitate the concentrations of 13C metabolites from an extract of polar metabolites from yeast that are cultured with 13C isotopically labeled glucose as the sole carbon source. This quantitated yeast extract is then used as an internal standard that allows for reliable quantification of targeted metabolites in biological samples while minimizing systematic error from matrix effects. This approach provides a robust method to quantitate polar metabolites in biological fluids and complements similar existing isotope dilution based methods, such as the commercially available Biocrates AbsoluteIDQ kits (Gieger et al., 2008) that primarily quantify non-polar lipids in biological samples.

The absolute quantitation of metabolite levels enabled by this protocol can allow for direct comparison of interstitial fluid composition in diverse tumor types, providing the opportunity to systematically interrogate nutrient availability in animal models of diverse cancers and human tumor samples. Further, the absolute quantification of metabolite levels in interstitial fluid allows for the generation of tissue culture media that mimics physiological conditions found in a tumor, thus expanding the range of in vitro/ ex vivo experiments that can be carried out under physiological nutrient conditions. Most broadly, this protocol provides a method to absolutely quantify many metabolites simultaneously in complex biological fluids, which can be used to study the metabolic composition of any biological material.

Materials and Reagents

  1. 50 ml conical tube (Falcon, catalog number: 14 959 49A)
  2. Lab tape (Thermo Fisher, catalog number: 15-901-20H)
  3. EDTA-coated plasma collection tubes (Sarstedt, catalog number: 41.1395.105)
  4. 1 ml 25G TB syringe (Becton Dickinson, catalog number: 309625)
  5. 20 μM mesh nylon filter (Spectrum Labs, catalog number: 148134)
  6. Whatman paper (Thermo Fisher, catalog number: 88600)
  7. Petri dishes (Corning, catalog number: 07 202 011)
  8. Eppendorf tubes (Corning, catalog number: 14 222 168)
  9. LC/MS sample vials (Thermo Fisher, catalog number: C4000-11)
  10. LC/MS vial caps (Thermo Fisher, catalog number: C5000-54B)
  11. Glassware (bottles, graduated cylinders, conical flasks) reserved for LCMS (not washed by central glass washing services, as this can introduce surfactant and other contaminants into your system)
  12. Pipettes (Gilson, catalog number: F167380)
  13. Mouse surgical kit (Thermo Fisher, catalog number: 14516249)
  14. Blood bank saline (Azer Scientific, catalog number: 16005-092)
  15. 70% Ethanol (Thermo Fisher, catalog number: 04-355-305)
  16. Acetonitrile LC/MS Optima 4 L (Fisher Scientific, catalog number: A955-4)
  17. Methanol LC/MS Optima 4 L (Fisher Scientific, catalog number: A456-4)
  18. Pierce formic acid (99%, LC/MS grade, Life Technologies, catalog number: 28905)
  19. Water LC/MS Optima 4 L (Fisher Scientific, catalog number: W64)
  20. Ammonium carbonate (Sigma-Aldrich, catalog number: 379999)
  21. Ammonium hydroxide solution (28%, Sigma-Aldrich, catalog number: 338818)
  22. Metabolomics amino acid standard mix (Cambridge Isotope Laboratories, Inc., catalog number: MSK-A2-1.2)
  23. 13C isotopically labeled yeast metabolite extract (Cambridge Isotope Laboratories, Inc., catalog number: ISO1)
  24. 13C3 lactate (Sigma-Aldrich, catalog number: 485926)
  25. 13C3 glycerol (Cambridge Isotope Laboratory, catalog number: CLM-1510)
  26. 13C6 15N2 cystine (Cambridge Isotope Laboratory, catalog number: CNLM-4244)
  27. 2H9 choline (Cambridge Isotope Laboratory, catalog number: DLM-549)
  28. 13C4 3-hydroxybutyrate (Cambridge Isotope Laboratory, catalog number: CLM-3853)
  29. 13C6 glucose (Cambridge Isotope Laboratory, catalog number: CLM-1396)
  30. 13C2 15N taurine (Cambridge Isotope Laboratory, catalog number: CNLM-10253)
  31. 2H3 creatinine (Cambridge Isotope Laboratory, catalog number: DLM-3653)
  32. 13C5 hypoxanthine (Cambridge Isotope Laboratory, catalog number: CLM-8042)
  33. 13C3 serine (Cambridge Isotope Laboratory, catalog number: CLM-1574)
  34. 13C2 glycine (Cambridge Isotope Laboratory, catalog number: CLM-1017)
  35. Chemical standard library pool 1
    Metabolite name
    Manufacturer
    Part#
    Alanine
    Sigma
    A7469
    Arginine
    Sigma
    A6969
    Asparagine
    Sigma
    A7094
    Aspartate
    Sigma
    A7219
    Carnitine
    Sigma
    C0283
    Citrulline
    Sigma
    C7629
    Cystine
    Sigma
    C7602
    Glutamate
    Sigma
    G8415
    Glutamine
    Sigma
    G3126
    Glycine
    Sigma
    G7126
    Histidine
    Sigma
    53319
    Hydroxyproline
    Sigma
    H54409
    Isoleucine
    Sigma
    I7403
    Leucine
    Sigma
    L8912
    Lysine
    Sigma
    L8662
    Methionine
    Sigma
    M5308
    Ornithine
    Sigma
    75469
    Phenylalanine
    Sigma
    P5482
    Proline
    Sigma
    P5607
    Serine
    Sigma
    S4311
    Taurine
    Sigma
    T0625
    Threonine
    Sigma
    T8441
    Tryptophan
    Sigma
    T8941
    Tyrosine
    Sigma
    T8566
    Valine
    Sigma
    V0513
    Lactate
    Sigma
    L7022
    Glucose
    Sigma
    G7528
  36. Chemical standard library pool 2
    Metabolite name
    Manufacturer
    Part#
    2-hydroxybutyric acid
    Sigma
    220116
    2-aminobutyric acid
    Sigma
    162663-25G
    AMP
    Sigma
    A1752
    Argininosuccinate
    Sigma
    A5707-50MG
    Betaine
    Sigma
    61962
    Biotin
    Sigma
    B4639
    Carnosine
    Sigma
    C9625-5G
    Choline
    Sigma
    C7017
    CMP
    Sigma
    C1006
    Creatine
    Sigma
    C0780-50G
    Cytidine
    Sigma
    C4654
    dTMP
    Sigma
    T7004-100MG
    Fructose
    Sigma
    F0127
    Glucose-1-phosphate
    Sigma
    G6750
    Glutathione
    Sigma
    G4251
    GMP
    Sigma
    G8377
    IMP
    Sigma
    57510-5G
    O-phosphoethanolamine
    Sigma
    P0503-1G
    Pyridoxal
    Sigma
    P9130-500MG
    Thiamine
    Sigma
    T4625
    trans-Urocanate
    Cayman
    16228
    UMP
    Sigma
    U6375-1G
    Xanthine
    Sigma
    X7375-10G
  37. Chemical standard library pool 3
    Metabolite name
    Manufacturer
    Part#
    3-hydroxybutyric acid
    Sigma
    H6501
    Acetylalanine
    Sigma
    A4625-1G
    Acetylaspartate
    Sigma
    00920-5G
    Acetylcarnitine
    Sigma
    A6706
    Acetylglutamine
    Sigma
    A9125-25G
    ADP
    Sigma
    A5285
    Allantoin
    Sigma
    05670-25G
    CDP
    Abcam
    ab146214-100 mg
    CDP-choline
    Alfa
    J64161-10 g
    Coenzyme A
    Sigma
    C4282
    Creatinine
    Sigma
    C4255-10MG
    gamma-aminobutyric acid
    Sigma
    A2129-10G
    GDP
    Sigma
    G7127
    Glutathione disulfide
    Sigma
    G4376
    Glycerate
    Sigma
    367494
    Hypoxanthine
    Sigma
    H9377
    myo-Inositol
    Sigma
    I5125
    NAD+
    Sigma
    N1511
    p-aminobenzoate
    Sigma
    A9878
    Phosphocholine
    Sigma
    P0378-5G
    Sorbitol
    Sigma
    W302902
    UDP
    Sigma
    94330-100MG
    UDP-glucose
    Sigma
    U4625-100MG
  38. Chemical standard library pool 4
    Metabolite name
    Manufacturer
    Part#
    Phenylacetylglutamine
    Cayman
    16724-25mg
    Acetylglutamate
    Sigma
    855642
    Acetylglycine
    Sigma
    A16300-5G
    Acetylmethionine
    Sigma
    01310-5G
    Asymmetric dimethylarginine
    Cayman
    80230
    ATP
    Sigma
    A2383
    CTP
    Sigma
    C1506
    dATP
    Sigma
    D6500
    dCTP
    Sigma
    D4635
    Deoxycytidine
    Sigma
    D3897
    Folic acid
    Sigma
    F8758
    GTP
    Sigma
    G8877
    Hypotaurine
    Sigma
    H1384-100MG
    Methionine sulfoxide
    Sigma
    M1126-1G
    Methylthioadenosine
    Sigma
    D5011-25MG
    Phosphocreatine
    Sigma
    P7936-1G
    Pyridoxine
    Sigma
    P9755
    Ribose-5-phosphate
    Sigma
    83875
    SAH
    Sigma
    A9384-25MG
    Thymidine
    Sigma
    T9250
    Trimethyllysine
    Sigma
    T1660-25MG
    Uridine
    Sigma
    T1660-25MG
    Uridine
    Sigma
    U3003
    UTP
    Sigma
    U6625
  39. Chemical standard library pool 5
    Metabolite name
    Manufacturer
    Part#
    3-phosphoglycerate
    Sigma
    P8877
    cis-aconitic acid
    Sigma
    A3412-1G
    Citrate
    Sigma
    754
    DHAP
    Sigma
    51269
    Fructose-1,6-bisphosphate
    Sigma
    F6803
    Fumarate
    Sigma
    240745
    Glucose-6-phosphate
    Sigma
    G7879
    Glycerol-3-phosphate
    Cayman
    20729-100 mg
    Guanidinoacetate
    Sigma
    G11608
    Kynurenine
    Sigma
    K8625
    Malate
    Sigma
    2288
    NADP+
    Sigma
    N0505
    Niacinamide
    Sigma
    72340
    2-oxoglutarate
    Sigma
    75890-25G
    Phosphoenolpyruvate
    Sigma
    P3637
    Pyruvate
    Sigma
    P5280
    Succinate
    Sigma
    S3674
    Uracil
    Sigma
    U0750
  40. Chemical standard library pool 6
    Metabolite name
    Manufacturer
    Part#
    3-hydroxyisobutyric acid
    Adipogen
    CDX-H0085-M250
    2-hydroxyglutarate
    Sigma
    H8378
    Aminoadipate
    Sigma
    A0637
    beta-alanine
    Sigma
    14064
    Carbamoylaspartate
    Alfa
    A17166-10 g
    Cystathionine
    Cayman
    16061-50 mg
    Cysteic acid
    Santa Cruz
    sc-485621
    FAD
    Sigma
    F6625
    Glycerophosphocholine
    Sigma
    G5291-50MG
    Inosine
    Sigma
    I4125
    Orotate
    Sigma
    O2875
    Pantothenate
    Sigma
    P5155
    Phosphoserine
    Fluka
    79710
    Riboflavin
    Sigma
    R9504
    UDP-GlcNAc
    Sigma
    U4375
    Uric acid
    Sigma
    U2625-25G
  41. Chemical standard library pool 7
    Metabolite name
    Manufacturer
    Part#
    Itaconic acid
    Sigma
    I29204
    Homocysteine
    TCI
    H0159
    2-oxobutyric acid
    Sigma
    K401
    2-hydroxybutyric acid
    Sigma
    220116
    Ascorbate
    Sigma
    A7506
    Sarcosine
    Sigma
    131776-100G
    Dimethylglycine
    Sigma
    D1156-5G
    N6-acetyllysine
    Sigma
    A4021-1G
    Pipecolate
    Sigma
    P45850-25G
    Indolelactate
    Sigma
    I5508-250MG-A
    Picolinate
    Sigma
    P42800-5G
    3-methyl-2-oxobutyrate
    Sigma
    198994-5G
    3-methyl-2-oxopentanoic acid
    Sigma
    198978-5G
    Formyl-methionine
    Sigma
    F3377-250MG
    2-aminobutyric acid
    Sigma
    A2536-1G
    Homocitrulline
    Santa Cruz
    sc-269298-100 mg
    gamma-glutamyl-alanine
    Sigma
    483834-500MG
    Mannose
    Sigma
    M6020-25G
    Cysteine-glycine (dipeptide)
    Sigma
    C0166-100MG
  42. Mobile Phase A (see Recipes)
  43. Mobile Phase B and Needle Wash (see Recipes)
  44. Rear Seal Wash (see Recipes)
  45. 80% methanol containing 13C-15N labeled amino acid mix (see Recipes)
  46. Extraction Buffer with isotopically labeled internal standards (EB) (see Recipes)
  47. Chemical standard library preparation (see Recipes)

Equipment

  1. Sorvall Legend X1R Refrigerated Centrifuge (Thermo Fisher, catalog number: 75004260)
  2. Sorvall Legend Micro 21 Refrigerated Centrifuge (Thermo Fisher, catalog number: 75002447)
  3. Mixer Mill (Retsch, catalog number: MM301)
  4. 50 ml Mixing Jar (Retsch, catalog number: 01.462.0216)
  5. 5 mM Grinding Balls (Retsch, catalog number: 05.368.0034)
  6. LP Vortex Mixer (Thermo Fisher, catalog number: 11676331)
  7. Analytical balance (Mettler Toledo, catalog number: AL54)
  8. Dionex Ultimate 3000 UHPLC equipped with RS Binary Pump, RS column oven and RS autosampler (Thermo Fisher Scientific, San Jose, CA)
  9. QExactive hybrid quadrupole-Orbitrap benchtop mass spectrometer equipped with an Ion Max ion source and HESI-II probe (Thermo Fisher Scientific, San Jose, CA)
  10. SeQuant® ZIC®-pHILIC 5 μm 150 x 2.1 mm polymeric analytical PEEK HPLC column (Millipore Sigma, catalog number: 1504600001)
  11. SeQuant® ZIC®-pHILIC 5 μm 20 x 2.1 mm PEEK Guard Kit with coupler (3 pc) (Millipore Sigma, catalog number: 15043800001)

Software

  1. Thermo Scientific Xcalibur 4.1 SP1 (Thermo Fisher Scientific)
  2. Microsoft Excel

Procedure

  1. Study design
    1. How many biological replicates are recommended?
      1. The number of biological replicates needed for studies will depend on the variability between samples. For animal studies, where a large number of variables can be controlled (i.e., tumor genetics, tumor size, animal genetics, animal diet, time of interstitial fluid isolation), variability will likely be smaller than for human samples. Additionally, the number of replicates required will depend on the intended purpose of the experiment to be performed. For example, if the intended purpose is to determine if there is a nutritional difference in the interstitial fluid between two tumor types, it is important to determine an effect size between the groups in addition to variability between samples in order to estimate sample sizes needed. We recommend generating pilot data or utilizing previously published data on TIF composition differences (Sullivan et al., 2019) to estimate effect sizes and utilizing power analysis software in Metaboanalyst (Chong et al., 2018) or other statistical analysis software to estimate the number of biological replicates needed.
      2. Additionally, when determining the number of biological replicates needed, it is important to note that not every tumor will necessarily yield interstitial fluid. In our experience, roughly 75% of murine pancreatic adenocarcinomas yielded tumor interstitial fluid (TIF) with volumes ranging from 5 to 180 μl of fluid (Sullivan et al., 2019). Therefore, additional samples may be required to achieve the required number of replicates determined from power analysis.
    2. We recommend harvesting TIF at the same time from all animals involved in a study. Circadian rhythm and food intake can alter plasma metabolite levels and therefore TIF metabolite levels, introducing additional variability (Dallmann et al., 2012; Abbondante et al., 2016). If TIF must be harvested from animals on different days, we recommend harvesting TIF at the same time during the day.
    3. We recommend two people work together to isolate TIF and cardiac blood to increase the speed of TIF harvest to prevent alterations in TIF composition due to prolonged periods of ischemia occurring between euthanasia and TIF harvest. In our own experiments, dissection was completed in ~2 min. and we found limited evidence of ischemia altering tumor metabolite levels (Sullivan et al., 2019).
    4. We have successfully isolated TIF using the protocol described in Procedure B from multiple genetically engineered and implantation mouse models of breast, lung, prostate, pancreas and melanoma cancers and others have isolated TIF from murine models of melanoma and breast cancer (Ho et al., 2015; Eil et al., 2016; Spinelli et al., 2017; Zhang et al., 2017) using similar protocols. Additionally, similar protocols have been used to isolate TIF from renal (Siska et al., 2017) and ovarian carcinomas (Haslene-Hox et al., 2011). Thus, we anticipate the TIF isolation protocol can be used successfully for a variety of tumor types, of mouse and human origin.
    5. The analysis described in Procedure C uses 7 separate chemical standard pools as described in (Sullivan et al., 2019) that enable the quantification of 149 metabolites commonly measured in biofluids (Lawton et al., 2008; Evans et al., 2009; Mazzone et al., 2016; Cantor et al., 2017). However, depending on experimental goals, the full analysis utilizing all 7 standard pools may not be required. Subsets of the chemical standard pools can be used that cover analytes of interest if the full analysis is not needed. Note though that individual metabolites in the standard pools provided in this protocol have been carefully selected so as to avoid metabolites with the same m/z (isomeric and isobaric species) being in the same pool. In addition, metabolites that could be generated by in-source fragmentation from larger metabolites have been separated.
    6. The description of the liquid chromatography-mass spectrometry analysis in this protocol (Procedure D) is a rough guideline for experienced operators of such instruments to perform the analysis described. Successful mass spectrometry analysis of the samples will require a trained UHPLC and Thermo Scientific hybrid quadrupole-Oribtrap mass spectrometer operator.

  2. Isolation of TIF and plasma from tumor bearing animals
    1. Prepare a TIF isolation tube (Figure 1 A).
      1. Take a nylon filter and place it over the top of a 50 ml conical tube.
      2. Tape the filter down using lab tape. Make sure the filter is affixed somewhat loosely to the top of the conical tube, such that the tumor can push the filter down slightly into the tube.


        Figure 1. Collecting TIF using nylon mesh filters attached to conical tubes. A. The filter is loosely affixed to the top of the conical tube using laboratory tape, so that the filter will sag slightly into the conical tube when a sample is placed on top of the filter. B. A sample on top of the filter and conical tube. C. After adding the sample to the filter, the lid of the conical tube is placed on top, but not screwed onto the conical. Instead, it is taped using laboratory tape in place. D. ~30 μl of tumor interstitial fluid (colored blue to here for contrast) collected in the conical tube after centrifugation.

    2. Prepare materials in advance to allow for rapid mouse dissection.
      1. Put pre-chilled (4 °C) saline (~25 ml) into a Petri dish for washing the tumor.
      2. Make a ~4 cm square piece of Whatman paper for drying the tumors after the saline rinse.
      3. Have a 25G TB syringe ready for cardiac blood collection.
      4. Label and pre-chill one EDTA-coated plasma collection tubes on ice for 10 min prior to mouse dissection.
    3. Euthanize the mouse by cervical dislocation.
    4. Spray around the incision site with 70% ethanol to prevent contamination of samples with hair.
    5. Quickly and cleanly dissect the tumor away from the animal. The exact procedure for the dissection will depend on the anatomical location of the tumor. One person should continue with the following steps while the other person can start on plasma isolation (Step B6).
      1. Place the tumor into the saline containing Petri dish to rinse the tumor.
      2. Blot the tumor dry on Whatman paper. Take care at this step to ensure all saline is blotted from the tumor, so that saline does not contaminate the TIF.
        If concerned about saline contamination during TIF isolation, we suggest the following additional step: add a non-natural metabolite such as norvaline to the saline. Continue with the remainder of the protocol as normal. Subsequently, when analyzing TIF samples, determine if the non-natural metabolite is present in the TIF sample. If the non-natural metabolite is detected this indicates saline contamination, whereas if the metabolite is not detected saline contamination is unlikely.
      3. Optional: Weigh the tumor if this information is needed. Tumor volume can also be determined by caliper measurements if needed.
      4. Place the tumor on top of the nylon mesh filter that is affixed to the conical tube (Figure 1B). Place the 50 ml conical lid over the tumor and tape the lid in place (Figure 1C).
      5. Centrifuge the tumor at 4 °C for 10 min at 106 x g using a refrigerated clinical centrifuge (e.g., Sorvall Legend X1R).
      6. Remove the 50 ml conical tube. If the isolation worked, there will be 10-50 μl of fluid in the bottom of the conical tube (Figure 1D). Keep the tube on ice.
        Note: Human tumors have been found to yield 5-150 μl of fluid per gram of tumor (Haslene-Hox et al., 2011). Similarly, we isolated 10-50 μl of fluid from murine pancreatic tumors weighing ~0.3-2.5 g (Sullivan et al., 2019). There is not an exact correlation between tumor size and TIF volume, as many factors likely influence interstitial volume. However, larger tumors are more likely to yield TIF in larger volumes. Tumors with large fluid filled cysts can give hundreds of μl of fluid per gram of tumor. Disregard these from analysis as it is unclear if the cystic fluid is representative of interstitial fluid.
      7. Remove the isolate fluid to a fresh Eppendorf tube. The fluid can be extracted directly for LC/MS analysis or frozen and stored at -80 °C for future analysis.
        We have analyzed TIF samples both before and after 2 months of storage at -80 °C (avoiding freeze-thaw cycles) and detected similar metabolite concentrations after storage (Sullivan et al., 2019). Thus, TIF samples can be stored for at least 2 months without freeze-thaw cycles prior to analysis.
      8. The tumor from which TIF was isolated can be used for additional analysis using other appropriate protocols.
        Note: We have successfully used tumors from which TIF was isolated for immunohistochemical, immunoblotting and flow cytometric analyses.
    6. Use the 25G TB syringe to isolate blood from the mouse heart by cardiac puncture. Cardiac puncture can be a difficult technique to perform. Detailed protocols with video documentation of cardiac puncture blood isolation have been previously published (Schroeder, 2019). If unfamiliar with this technique, we recommend utilizing these resources for more detailed information on isolating blood in this manner.
      1. Dissect open the thoracic cavity.
      2. Insert the syringe into the ventricle.
      3. Slowly withdraw blood to prevent collapse of the heart.
      4. Remove the needle from the syringe to prevent cell lysis when expelling the cells from the syringe.
      5. Expel the blood into the EDTA-coated plasma collection tube.
      6. Keep plasma on ice.
      7. Spin the EDTA-coated plasma collection tubes at 845 x g in benchtop centrifuge (e.g., Sorvall Legend Micro 21) for 15 min at 4 °C.
      8. Remove the plasma from the pelleted blood cells and put into fresh Eppendorf tube.
      9. Extract this plasma directly for LC/MS analysis or freeze and store at -80 °C for future analysis.
        Note: Previous studies have found that plasma samples can be stored at -80 °C (without freeze-thaw cycles) for up to 30 months without significant alterations in the levels of many metabolites (Stevens et al., 2019). Thus, plasma samples can be stored for many months prior to analysis.

  3. Extraction of metabolites from TIF and plasma
    1. Prepare libraries of pooled chemical standards that include the metabolites to be quantified. See Recipes section and Tables 5-11 for details on how libraries were compounded in (Sullivan et al., 2019).
    2. Prepare metabolite extraction buffer (EB) (Recipe 5) with appropriate isotopically labeled internal standards. See Recipes section for details on making EB with isotopic standards as described in (Sullivan et al., 2019).
      Note: Make enough EB for the number of samples and standards you have plus an additional 10%, so as not to run out of EB before extracting metabolites from every sample. 45 μl of EB is needed for each sample and standard pool dilution. Isotopically labeled metabolite standards in the EB are not indefinitely stable. Prepare EB fresh prior to each experiment.
    3. Prepare dilutions of chemical standard libraries in HPLC grade water. The highest concentration should be 5 mM.
    4. Next, make dilution series of each standard pool in HPLC grade water as listed below in Step C5. Keep these on ice prior to extraction. Note that multiple dilutions of the chemical standard libraries are required for construction of standard curves relating known metabolite concentration to LC/MS response, which is necessary for downstream analysis of metabolite concentrations. Below we recommend a scheme to generate 8-point standard curves covering physiological concentrations of metabolites, but variations are possible.
    5. The 5 mM pool will not be always in solution for each pool, therefore vortex vigorously and immediately pipette from this mixture in order to prevent error from settling particles:
      1. Take 20 μl of 5 mM stock and dilute into 80 μl HPLC grade water to yield a 1 mM solution.
      2. Take 30 μl of 1 mM stock and dilute into 70 μl HPLC grade water to yield a 300 μM solution.
      3. Take 33.33 μl of 300 μM stock and dilute into 66.67 μl HPLC grade water to yield a 100 μM solution.
      4. Take 30 μl of 100 µM stock and dilute into 70 μl HPLC grade water to yield a 30 μM solution.
      5. Take 33.33 μl of 30 μM stock and dilute into 66.67 μl HPLC grade water to yield a 10 μM solution.
      6. Take 30 μl of 10 μM stock and dilute into 70 μl HPLC grade water to yield a 3 µM solution.
      7. Take 33.33 μl of 3 μM stock and dilute into 66.67 μl HPLC grade water to yield a 1 μM solution.
    6. Thaw the TIF and plasma samples on ice.
    7. Add 5 μl of each TIF sample, plasma sample and chemical standard library dilution (Recipe 6) to a fresh Eppendorf tube. Keep on ice.
    8. Add 45 μl of EB to each sample/standard. Keep on ice.
    9. Vortex all the samples for 10 min at maximum speed at 4 °C.
    10. Spin down all samples for 10 min at 21,000 x g at 4 °C.
    11. Take 20 μl of the mixtures from the Eppendorf tube and add to an LC/MS sample vial. Cap the vial.
      Note: A minimum of 15 μl is needed in the LC/MS vial to ensure correct and accurate injection by the autosampler. The vials described in Materials and Reagents contain fused inserts. Vials without inserts will require larger volumes. 
    12. Freeze the remaining sample in the Eppendorf tubes and store at -80 °C. This sample can be run later if the initial LC/MS is not successful.

  4. Liquid chromatography-mass spectrometry analysis of extracted metabolites
    1. Start off with a clean system (use appropriate LC cleaning methods in place in your lab).
    2. Calculate the amount of Mobile Phase A (Recipe 1) required and prepare fresh on the day of analysis. Store this for no more than one week.
      Note: Depending on your system, you will use ~2 ml per injection and require an additional 50-100 ml in the bottle. Do not forget to make enough Mobile Phase A for additional injection types such as solvent blanks and system suitability tests that must be run in addition to the samples.
    3. Calculate the amount of Mobile Phase B (Recipe 2) required and prepare more if needed. Depending on your system, you will need ~2 ml per injection plus an additional 50-100 ml in the bottle.
    4. Check the level of rear seal wash (Recipe 3) and top up if needed.
    5. If using an UHPLC system that has a separate needle wash, fill this with acetonitrile.
    6. Connect a SeQuant® ZIC®-pHILIC 5 μm 150 x 2.1 mm analytical column to the Guard column using the connector supplied in the Guard kit.
    7. Connect the column and guard to your UHPLC system using standard techniques.
    8. Set the column oven temperature to 25 °C. 
    9. Set the autosampler temperature to 4 °C.
    10. Set initial conditions: set the flow rate to 0.150 ml/min with 80% B. Record initial pressure value.
      Note: ZIC-pHILIC columns cannot tolerate such high back pressures and injection volumes as typical reverse phase columns. Keep an eye on the back pressure and do not let it exceed the maximum pressure recommended by the manufacturer. It is good practice to set a maximum pressure in your method that is below that set by the manufacturer to avoid damage to the column.
    11. Equilibrate the column with starting conditions (80% B) for 30 min prior to running anything on the system. 
    12. Check the mass calibration on the mass spectrometer. If the mass has not been calibrated within the last week, or if it fails the mass check, recalibrate using the standard calibration mixes recommended by the manufacturer. In addition, perform a custom low-mass calibration by spiking glycine and aspartate into the calibration mix, or as recommended by the manufacturer. 
    13. Ensure your entire LCMS system performance is acceptable by running system suitability tests, such as injecting a mixture of amino acids onto your column and into the MS. Check for signal intensity as well as peak shape and separation.
    14. Use the conditions shown in Table 1 below for the UHPLC gradient:

      Table 1. LC parameters
      Time (min)
      Flow rate (ml/min)
      %B
      0.00
      0.150
      80
      20.0
      0.150
      20
      20.5
      0.150
      80
      28.0
      0.150
      80

    15. Operate the mass spectrometer in full-scan, polarity-switching mode, with a scan range of 70-1000 m/z. Include an additional narrow-range scan from 220 to 700 m/z in negative mode to improve detection of nucleotides. Use the parameters shown below in Tables 2 and 3 for the MS:

      Table 2. MS source parameters
      Parameter
      Setting
      Spray voltage
      3.0 kV (pos); 3.1 kV (neg)
      Heated capillary
      275 °C
      HESI probe
      350 °C
      Sheath gas
      40 units
      Aux gas
      15 units
      Sweep gas
      1 unit

      Table 3. MS scan parameters
      Parameter
      Setting
      Resolution
      70,000
      AGC target
      1E6
      Max IT
      20 ms

    16. Note that in this particular study, we had previously collected MS/MS data for each metabolite being quantified, and used this to confirm retention times using a library of chemical standards. If adding new metabolites to your standard pools, collect MS/MS data on the standard itself, as well as on a pooled biological sample to help confirm peak identification. 
    17. Write your sequence (sample run order) using Thermo Scientific Xcalibur Sequence Setup View.
      Note: Given the extremely low volume of samples used in this method, the sequence differs from typical sequences which will include column conditioning injections, as well as quality control pooled samples. As multiple standard curves are run and each sample includes 13C-labeled internal standards, we chose to forgo using precious sample to create QC pools and instead determine linearity and consistency of metabolite detection using the standard curves and the 13C internal standards.
      1. Start off by injecting several water blanks to ensure system is clean from carry over and contaminants.
      2. Include solvent blanks, using the 75/25/0.1 acetonitrile/methanol/formic acid mix that was used to make the extraction mix.
      3. Follow with a system suitability test (SST) injection. We use 80% methanol containing 13C-15N labeled amino acid mix (Recipe 4).
      4. Add the samples to your sequence and follow with the standard curves, starting with the lowest concentration and working up to the highest concentration for each curve. Separate each curve with solvent blank and check for carry-over.
      5. Insert additional SST injections every 8-10 samples. These will be used as QCs to ensure no loss of signal over time.
      6. Set the injection volume to 2 μl for each injection type.
      7. Set the instrument method to the appropriate method.
      8. Save the sequence file.
      9. Randomize sample running order to decrease the chance of signal loss over time. Export the sequence as a .csv file. Open in Microsoft Excel, cut and paste the samples into a new tab, leaving behind the blanks, STTs and standard curves. Add an additional column and use the = rand () function to create random numbers for each of the samples. Now sort the samples from smallest to largest using the random number values. Cut and paste back into the previous sequence containing the blanks, SSTs and the standard curves. Save the .csv file with “random” in the file name. Import the new randomized sequence back into Xcalibur Sequence View and save using “random” in the file name.
      10. Place your sample vials in the autosampler in the vial positions according to the sequence. Use the non-randomized sequence to check vial positions for your samples.
      11. Ensure the solvent blank vials and SST vials contain enough volume for multiple injections.
    18. Run the sequence.
      Note: For examples of expected outputs from the LC/MS analysis of TIF and plasma samples, LC/MS data from (Sullivan et al., 2019) is available at https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Project&ProjectID=PR000750.

Data analysis

  1. Identify metabolite peaks. This protocol will describe peak identification in Thermo Scientific Xcalibur, but could be adapted with any other peak identification method.
    1. Generate a processing method file that will be used to identify peaks for each metabolite of interest:
      1. Create a new processing method.
      2. Load a .raw file containing LC/MS data derived from an external standard sample that contains the metabolite of interest as well as a 13C internal standard for that metabolite.
        Note: Typically using an external standard sample that is in the middle of the standard curve works best; sometimes high concentrations points on the standard curve have poor quality peaks.
      3. Calculate the exact mass of the metabolite of interest.
        Notes:
        1. Exact mass is determined by summing the masses of the most abundant isotopes of each element in a compound. For instance, the exact mass of CO2 would be the summed masses of a carbon-12 atom (12.000) + two oxygen-16 atoms (15.995 + 15.995) = 43.990. This exact mass of a compound will differ from its molecular weight; the molecular weight of an element is derived by averaging the masses of each of the isotopes of that element, weighted by the abundance of each isotope in nature.
        2. If using Thermo Scientific Xcalibur, calculate the exact mass using the Isotope Simulation tab in the QualBrowser module. Enter the chemical formula, ensure the “adduct” check box in unchecked and select “New”. Ensure that the software global settings are set to a mass tolerance of 5 ppm and mass precision is set to 5 decimal places. Ideally, the peak integration software that you are using should calculate this for you.
      4. Calculate the mass to charge ratio (m/z) of the metabolite of interest in positive and negative ionization mode.
        Notes: 
        1. For analysis of small polar metabolites, the most common ions will be those that have gained or lost a single proton and most molecules will have a charge state of 1.
        2. If using Thermo Scientific Xcalibur, calculate the m/z using the Isotope Simulator in the QualBrowser module by entering the formula, checking the “adduct” box and selecting a charge of either +1 or -1, depending on whether you are calculating the m/z in positive or negative mode, respectively.
        3. There are a variety of online tools available that will provide exact mass information, as well as calculate m/z for a variety of different adducts. The most comprehensive is the Metlin data base (Guijas et al., 2018): https://metlin.scripps.edu/landing_page.php?pgcontent=mainPage.
      5. In the processing method, select either positive ionization mode or negative ionization mode depending on whether you will be searching for a positively charged ion or a negatively charged ion.
        Note: Some metabolites are more easily detected in positive or negative mode. A list of recommendations for which mode to use for a variety of metabolites is located in Supplementary File 1 of (Sullivan et al., 2019). If no recommendations are available, empirically determine which method gives better detection by trying both.
      6. Identify and validate the retention time of each metabolite:
        1. Search for the exact mass of the ion of interest.
        2. Note the retention time of any peaks that match the exact mass of the ion of interest within 5 ppm.
        3. Open a .raw file of a different external standard sample with the metabolite of interest at a lower concentration.
          1)
          Note which peaks that match the exact mass of the ion of interest decrease in area.
          2)
          Repeat with each of the external standard samples that contain the metabolite of interest, checking which peak areas track with the expected amount of the metabolite.
          3)
          Refer to MS/MS data to confirm peak identification.
        4. Open a .raw file that does not contain the metabolite of interest. Ensure that any candidate peaks are not present in this .raw file.
        5. Search for the exact mass of the 13C labeled version of the ion of interest.
          Note: This peak should be approximately the same area in all samples.
        6. Check that the retention time of the 13C labeled standard peak exactly matches that of the candidate peak.
        7. Repeat for all metabolites of interest.
      7. Assign 13C labeled standards as internal standards for their corresponding 12C metabolites.
        For metabolites with no 13C internal standard, assign a 13C metabolite with a similar retention time as the internal standard.
    2. Use the processing method to pick and validate peaks for all metabolites in all LC/MS data files (both experimental samples and external standards):
      1. Once all peaks have been automatically picked, manually inspect every peak for each metabolite and for each sample to ensure that all peaks have been correctly identified. Some common examples of errors that occur with automatic peak picking algorithms:
        1. Incorrect peak was picked: this can occur for isobaric compounds with similar retention times, such as leucine and isoleucine.
        2. Peak was not fully picked from baseline to baseline.
        3. Peak was picked but overlaps with a second peak: this occasionally happens where biological samples have an overlapping peak that was not present in the external standards. If this is the case, this metabolite should not be quantitated using this LC/MS method and an alternative method of chromatographic separation should be identified.
      2. Export the ratio of peak areas for the sample versus the 13C internal standard to Microsoft Excel or your data processing software of choice.

  2. Determine the relationship between relative peak area and concentration of metabolite in external standards.
    1. Calculate the exact concentration of each metabolite in each point on the external standard curve based on the amount that was weighed out.
    2. Generate a graph of metabolite concentration in each external standard sample versus the relative peak area of the metabolite.
    3. Check if the relative peak area of the metabolite increases linearly with concentration:
      1. Fit a linear regression to the graph.
      2. The R2 value for the linear regression should be ≥ 0.995.
        Metabolites often respond non-linearly at high concentrations. If the standard curve has points that are much higher than the concentrations present in experimental samples, the highest points on the standard curve can be removed. Just ensure that the relative peak areas for all samples fall within the linear range of the standard curve.
      3. Non-linear metabolites should be excluded from quantitative analysis, as this lack of linearity will prevent accurate quantitation by isotope dilution.

  3. Determine the concentrations of the internal standards that were added to all samples.
    Solve for the concentration of 13C internal standard present in each external standard sample using the following relationship:



    Note: This relationship can be used to calculate the concentration of the 13C internal standard in each of the external standard samples; the same concentration should be present in each. To derive the most accurate value for the concentration of the 13C internal standard, average the concentrations derived from the external standard points most similar in concentration to the experimental samples.

  4. Calculate the concentration of each metabolite in the experimental samples by isotope dilution. Solve for the concentration of the 12C metabolite using the same relationship defined in step C.

  5. Calculate the semi-quantitative concentration of all other analytes using the external standard curves. These values are considered semi-quantitative as they are subject to matrix effects arising from the biological samples being compared to external standards dissolved in water. These matrix effects can be substantial (Sullivan et al., 2019).
    1. Calculate the slope and intercept of the linear regression calculated in step B3a.
    2. Use this slope and intercept to calculate the semi-quantitative value approximating the concentration of the metabolite.
    3. Manually evaluate the concentrations derived from this calculation:
      1. Check that the value of each metabolite is zero in the external standard samples that do not contain that metabolite.
      2. Any data that shows a negative concentration should be removed.

  6. Perform statistical analysis of the data using Metaboanalyst (https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml) (Chong et al., 2018) or another program for statistical analysis.
    1. Auto-scale the data (mean-center and divide by the standard deviation of each concentration).
    2. To broadly compare if there are differences in the metabolites present in two sample types, perform principal component analysis or hierarchical clustering.
    3. To identify specific metabolites that differ in concentration between sample types, generate a volcano plot in which a raw P-value of 0.01 and a fold change of 1.5 are used to identify significantly altered metabolites.

Recipes

  1. Mobile Phase A
    20 mM ammonium carbonate
    0.1% ammonium hydroxide (pH 9.4-9.6)
    Optima LC/MS water
  2. Mobile Phase B and Needle Wash
    Optima LC/MS acetonitrile
  3. Rear Seal Wash
    10% Optima LC/MS methanol
    Optima LC/MS water
  4. 80% methanol (containing 13C-15N amino acid standard mix) (200 ml)
    160 ml Optima LC/MS methanol
    40 ml Optima LC/MS water
    40 μl Metabolomics Amino Acid Standard Mix
  5. Extraction Buffer with isotopically labeled internal standards (EB) (Table 4)
    Notes:
    1. This is the recipe containing isotopically labeled internal standards for analysis of metabolites as in (Sullivan et al., 2019). If analysis of other metabolites using stable isotope internal standards is desired, purchase or synthesize the desired isotopically labeled metabolite and add it to the Extraction Buffer. Quantification with isotopically labeled standards performs best when the abundance of the isotopically labeled metabolite is similar to the unlabeled metabolite to be quantified. Therefore, when adding isotopically labeled internal standards, add the isotope such that it will be at roughly a similar abundance as the unlabeled metabolite when the sample is diluted in the Extraction Buffer.
    2. This recipe is for 180 samples at 45 μl per sample for a total of 8,100 μl in volume. Adjust the volumes accordingly as needed for the number of samples you intend to analyze. After adding all components, vortex briefly to ensure EB is well mixed, and store on ice while in use. Make fresh prior to each experiment. Remember to include extra 75/25/0.1 acetonitrile/methanol/formic acid for use as solvent blanks in your calculations.

      Table 4. Extraction Buffer (EB) composition
      Component
      Volume added   
      Final concentration
      HPLC grade acetonitrile
      5771.25 μl
      71.25%
      HPLC grade methanol
      1923.75 μl
      23.75%
      HPLC grade formic acid
      15.39 μl
      1.9%
      ~15 mg of isotopically labeled yeast extract (ISO1) dissolved in 1.5 ml of HPLC grade water.
      Note: After adding water to the yeast extract, dissolve the yeast extract by vortexing and/or
      rocking the yeast extract and water at 4 °C for approximately 30 min. Solution can be stored
      at -80 °C although some metabolites will degrade over time (see manufacturer’s instructions)

      405 μl
      5%
      2 mM solution of 2H9 choline prepared in HPLC grade water (stored at -20 °C)
      4.03 μl
      1 μM
      50 mM solution of 13C4 3-hydroxybutyrate prepared in HPLC grade water (stored at -20 °C)
      0.81 μl
      5 μM
      200 μM solution of 13C6 15N2 cystine prepared in HPLC grade water (stored at -20 °C)
      81 μl
      2 μM
      100 mM solution of 13C3 lactate prepared in HPLC grade water (stored at -20 °C)
      16.2 μl
      200 μM
      57.3 mM solution of 13C6 glucose prepared in HPLC grade water (stored at -20 °C)
      7.05 μl
      50 μM
      100 mM solution of 13C3 serine prepared in HPLC grade water (stored at -20 °C)
      1.62 μl
      20 μM
      750 mM solution of 13C2 glycine prepared in HPLC grade water (stored at -20 °C)
      1.62 μl
      150 μM
      2 mM solution of 13C5 hypoxanthine prepared in HPLC grade water (stored at -20 °C)
      2.02 μl
      0.5 μM
      200 mM solution of 13C2 15N taurine prepared in HPLC grade water (stored at -20 °C)
      2.02 μl
      50 μM
      60 mM solution of 13C3 glycerol prepared in HPLC grade water (stored at -20 °C)
      2.02 μl
      15 μM
      4 mM solution of 2H3 creatinine prepared in HPLC grade water (stored at -20 °C)
      2.02 μl
      1 μM


  6. Chemical standard library preparation
    Below are recipes for the preparation of chemical standard libraries described in (Sullivan et al., 2019). To prepare these chemical libraries, purchase the chemicals from the listed supplier and weigh them out as indicated, placing each metabolite into a 50 ml mixing mill jar. Mix the combined metabolites using a Mixer Mill MM301 with five 5 mm diameter stainless steel grinding balls. Perform 6 cycles of 1 min mixing at 25 Hz followed by 3 min resting. Store the now mixed chemical standard library powder stocks at -20 °C prior to use. For use, resuspend each mixed chemical library in HPLC grade water at 5 mM concentration as indicated for each library below.
    Custom chemical standard libraries can be produced by acquiring desired pure chemical standards and mixing the pure chemical standards in equimolar amounts. When generating libraries, it is important to ensure that each library will not contain metabolites that have the same exact mass, as it is not then possible to determine the correct retention time for both metabolites when compounded into the same library. Consider putting these metabolites into separate pooled libraries (Tables 5-11).

    Table 5. Chemical standard library pool 1
    Metabolite name
    Molecular weight of metabolite
    Molecular weight of chemical standard
    Amount to weigh (mg)
    Alanine
    89.09
    89.09
    429.99
    Arginine
    174.2
    210.66
    1016.75
    Asparagine
    132.12
    150.13
    724.60
    Aspartate
    133.11
    133.11
    642.45
    Carnitine
    161.199
    197.66
    954.00
    Citrulline
    175.2
    175.2
    845.60
    Cystine
    240.3
    240.3
    1159.80
    Glutamate
    147.13
    147.13
    710.12
    Glutamine
    146.14
    146.14
    705.34
    Glycine
    75.066
    75.066
    362.30
    Histidine
    155.1546
    155.1546
    748.85
    Hydroxyproline
    131.13
    131.13
    632.89
    Isoleucine
    131.1729
    131.1729
    633.10
    Leucine
    131.17
    131.1729
    633.10
    Lysine
    146.19
    182.65
    881.56
    Methionine
    149.21
    149.21
    720.16
    Ornithine
    132.16
    168.62
    813.84
    Phenylalanine
    165.19
    165.19
    797.28
    Proline
    115.13
    115.13
    555.67
    Serine
    105.09
    105.09
    507.21
    Taurine
    125.15
    125.15
    604.03
    Threonine
    119.119
    119.119
    574.92
    Tryptophan
    204.225
    204.225
    985.69
    Tyrosine
    181.19
    181.19
    874.51
    Valine
    117.151
    117.151
    565.42
    Lactate
    90.09
    112.06
    540.85
    Glucose
    180.1559
    180.1559
    869.52
    Note: Dissolve this pool at 20.19 mg/ml for 5 mM solution.

    Table 6. Chemical standard library pool 2
    Metabolite name
    Molecular weight of metabolite
    Molecular weight of chemical standard
    Amount to weigh (mg)
    2-hydroxybutyric acid
    104.1
    126.09
    12.61
    2-aminobutyric acid
    103.12
    103.12
    10.31
    AMP
    347.2212
    347.22
    34.72
    Argininosuccinate
    290.273
    334.24
    33.42
    Betaine
    117.1463
    117.15
    11.71
    Biotin
    244.31
    244.31
    24.43
    Carnosine
    226.2324
    226.23
    22.62
    Choline
    104.1708
    139.62
    13.96
    CMP
    323.1965
    367.16
    36.72
    Creatine
    131.133
    131.13
    13.11
    Cytidine
    243.2166
    243.22
    24.32
    dTMP
    320.1926
    366.17
    36.62
    Fructose
    180.16
    180.16
    18.02
    Glucose-1-phosphate
    260.135
    336.32
    33.63
    Glutathione
    307.3235
    307.32
    30.73
    GMP
    363.22
    407.18
    40.72
    IMP
    348.206
    392.17
    39.22
    O-phosphoethanolamine
    141.063
    141.06
    14.11
    Pyridoxal
    167.16
    203.62
    20.36
    Thiamine
    265.35
    337.23
    33.72
    trans-Urocanate
    137.118
    137.12
    13.71
    UMP
    324.1813
    368.15
    36.82
    Xanthine
    152.11
    152.11
    15.21
    Note: Dissolve this pool at 28.54 mg/ml for 5 mM solution.

    Table 7. Chemical standard library pool 3
    Metabolite name
    Molecular weight
    of metabolite

    Molecular weight
    of chemical standard

    Amount to weigh (mg)
    3-hydroxybutyric acid
    104.1045
    126.09
    252.18
    Acetylalanine
    131.1299
    131.13
    262.26
    Acetylaspartate
    175.139
    175.14
    350.28
    Acetylcarnitine
    203.2356
    239.70
    479.40
    Acetylglutamine
    188.183
    188.18
    376.36
    ADP
    427.203
    501.32
    1002.64
    Allantoin
    158.121
    158.121
    316.24
    CDP
    403.177
    403.20
    806.40
    CDP-choline
    489.332
    510.31
    1020.62
    Coenzyme A
    767.535
    767.53
    1535.06
    Creatinine
    113.12
    113.12
    226.24
    gamma-aminobutyric acid
    103.12
    103.12
    206.24
    GDP
    443.201
    443.20
    886.40
    Glutathione disulfide
    612.631
    612.63
    1225.26
    Glycerate
    106.0773
    286.25
    572.50
    Hypoxanthine
    136.1115
    136.11
    272.22
    myo-Inositol
    180.16
    180.16
    360.32
    NAD+
    663.43
    663.43
    1326.86
    p-aminobenzoate
    137.138
    137.14
    274.28
    Phosphocholine
    184.152
    329.73
    659.46
    Sorbitol
    182.17
    182.17
    364.34
    UDP
    404.1612
    448.12
    896.24
    UDP-glucose
    566.302
    610.27
    1220.54
    Note: Dissolve this pool at 37.23 mg/ml for 5 mM solution.

    Table 8. Chemical standard library pool 4
    Metabolite name
    Molecular weight of metabolite
    Molecular weight of chemical standard
    Amount to weigh (mg)
    Phenylacetylglutamine
    264.3
    264.3
    17.17
    Acetylglutamate
    189.1659
    189.1659
    12.29




    Acetylglycine
    117.1033
    117.1033
    7.61
    Acetylmethionine
    191.245
    191.245
    12.43
    Asymmetric dimethylarginine
    202.25
    275.2
    17.88
    ATP 507.18
    551.14
    35.82
    CTP 483.1563
    527.12
    34.26
    dATP
    491.2
    535.15
    34.78
    dCTP
    467.2
    511.12
    33.22
    Deoxycytidine
    227.2172
    227.2172
    14.76
    Folic acid
    441.3975
    441.3975
    28.69
    GTP
    523.2
    523.18
    34.00
    Hypotaurine
    109.1475
    109.1475
    7.09
    Methionine sulfoxide
    165.21
    165.21
    10.73
    Methylthioadenosine
    297.3335
    297.3335
    19.32
    Phosphocreatine
    211.114
    255.08
    16.58
    Pyridoxine
    169.18
    205.64
    13.36
    Ribose-5-phosphate
    230.11
    310.1
    20.15
    SAH
    384.4
    384.41
    24.98
    Thymidine
    242.2286
    242.2286
    15.74
    Trimethyllysine
    189.279
    224.73
    14.60
    Uridine
    244.2014
    244.2014
    15.87
    UTP
    484.1411
    559.09
    36.34
    Note: Dissolve this pool at 36.75 mg/ml for 5 mM solution.

    Table 9. Chemical standard library pool 5
    Metabolite name
    Molecular weight of metabolite
    Molecular weight of chemical standard
    Amount to weigh (mg)
    3-phosphoglycerate
    186.06
    230.02
    57.51
    cis-aconitic acid
    174.108
    174.11
    43.53
    Citrate
    192.124
    294.10
    73.53
    DHAP
    170.06
    180.19
    45.05
    Fructose-1,6-bisphosphate
    340.1157
    406.06
    101.52
    Fumarate
    116.07
    116.07
    29.02
    Glucose-6-phosphate
    260.135
    282.12
    70.53
    Glycerol-3-phosphate
    172.0737
    370.40
    92.60
    Guanidinoacetate
    117.1066
    117.11
    29.28
    Kynurenine
    208.2139
    208.21
    52.05
    Malate
    134.0874
    134.09
    33.52
    NADP+
    744.413
    765.39
    191.35
    Niacinamide
    122.12
    122.12
    30.53
    2-oxoglutarate
    146.11
    146.11
    36.53
    Phosphoenolpyruvate
    168.042
    267.22
    66.81
    Pyruvate
    88.06
    110.04
    27.51
    Succinate
    118.09
    118.09
    29.52
    Uracil
    112.0868
    112.09
    28.02
    Note: Dissolve this pool at 20.77 mg/ml for 5 mM solution.

    Table 10. Chemical standard library pool 6
    Metabolite name
    Molecular weight of metabolite
    Molecular weight of chemical standard
    Amount to weigh (mg)
    3-hydroxyisobutyric acid
    104.1045
    126.09
    22.07
    2-hydroxyglutarate
    148.114
    192.10
    33.62
    Aminoadipate
    161.156
    161.16
    28.20
    beta-alanine
    89.093
    89.09
    15.59
    Carbamoylaspartate
    176.128
    176.13
    30.82
    Cystathionine
    222.263
    222.26
    38.90
    Cysteic acid
    169.16
    169.16
    29.60
    FAD
    785.5497
    829.51
    145.16
    Glycerophosphocholine
    258.231
    257.22
    45.01
    Inosine
    268.229
    268.23
    46.94
    Orotate
    156.1
    194.19
    33.98
    Pantothenate
    219.23
    238.27
    41.70
    Phosphoserine
    185.07
    185.07
    32.39
    Riboflavin
    376.369
    376.37
    65.86
    UDP-GlcNAc
    607.3537
    651.32
    113.98
    Uric acid
    168.1103
    168.11
    29.42
    Note: Dissolve this pool at 21.52 mg/ml for 5 mM solution.

    Table 11. Chemical standard library pool 7
    Metabolite name
    Molecular weight of metabolite
    Molecular weight of chemical standard
    Amount to weigh (mg)
    Itaconic acid
    130.0987
    130.10
    52.04
    Homocysteine
    135.185
    135.19
    54.07
    2-oxobutyric acid
    102.0886
    102.09
    40.84
    2-hydroxybutyric acid
    104.1045
    126.09
    50.44
    Ascorbate
    176.1241
    198.11
    79.24
    Sarcosine
    89.0932
    89.09
    35.64
    Dimethylglycine
    103.1198
    103.12
    41.25
    N6-acetyllysine
    188.2242
    188.22
    75.29
    Pipecolate
    129.157
    129.16
    51.66
    Indolelactate
    205.2099
    205.21
    82.08
    Picolinate
    123.1094
    123.11
    49.24
    3-methyl-2-oxobutyrate
    116.1152
    138.10
    55.24
    3-methyl-2-oxopentanoic acid
    130.1418
    152.12
    60.85
    Formyl-methionine
    177.221
    177.22
    70.89
    2-aminobutyric acid
    103.1198
    103.12
    41.25
    Homocitrulline
    189.2123
    189.21
    75.68
    gamma-glutamyl-alanine
    217.2224
    218.21
    87.28
    Mannose
    180.16
    180.16
    72.06
    Cysteine-glycine (dipeptide)
    178.21
    178.21
    71.28
    Note: Dissolve this pool at 14.33 mg/ml for 5 mM solution.

Acknowledgments

This protocol is based on our previously published study (Sullivan et al., 2019). This work has been supported by a grant to AM from the NIH (F32CA213810). MRS was supported by T32GM007287 and acknowledges additional support from an MIT Koch Institute Graduate Fellowship.

Competing interests

The authors report no competing interests. CAL is a paid consultant for ReviveMed.

Ethics

This study was performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals. All animal experiments were performed using protocols (#1115-110-18) that were approved by the MIT Committee on Animal Care (IACUC). All surgeries were performed using isoflurane anesthesia administered by vaporizer and every effort was made to minimize suffering.

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Copyright Sullivan et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
How to cite:  Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Sullivan, M. R., Lewis, C. A. and Muir, A. (2019). Isolation and Quantification of Metabolite Levels in Murine Tumor Interstitial Fluid by LC/MS . Bio-protocol 9(22): e3427. DOI: 10.21769/BioProtoc.3427.
  2. Sullivan, M. R., Danai, L. V., Lewis, C. A., Chan, S. H., Gui, D. Y., Kunchok, T., Dennstedt, E. A., Vander Heiden, M. G. and Muir, A. (2019). Quantification of microenvironmental metabolites in murine cancers reveals determinants of tumor nutrient availability. Elife 8: e44235.
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