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Apr 2019

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Isolation and Quantification of Metabolite Levels in Murine Tumor Interstitial Fluid by LC/MS
LC/MS法对小鼠肿瘤间质液中代谢产物的分离和定量   

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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 (营养物), Metabolomics (代谢组学), Microenvironment (微环境), Interstitial fluid (组织液), Cancer metabolism (肿瘤代谢), Mass spectrometry (质谱法)

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.

References

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简介

癌症是一种以新陈代谢发生改变为特征的疾病,人们对了解肿瘤的代谢产生了新的兴趣。尽管营养物的可用性是肿瘤代谢的关键决定因素,但对于肿瘤微环境中可直接用于癌细胞的营养物的系统研究很少。表征肿瘤组织液中存在的代谢物的先前工作仅限于在有限数量的样品中测量少量营养物,例如葡萄糖和乳酸。在这里,我们采用了一种基于离心的肿瘤组织液分离方法,该方法易于适用于多种样品类型,并采用了一种基于质谱的方法对组织液样品中许多代谢产物进行绝对定量。在这种方法中,使用同位素稀释法和外标校准通过液相色谱-质谱法(LC / MS)分析肿瘤间质液(TIF),以得出间质液中目标代谢物的绝对浓度。同位素稀释的使用可以对代谢物进行精确的绝对定量,因为由于基质效应,根据代谢物所含流体的组成会改变代谢物的表观浓度,因此其他定量方法不足以确定生物流体中的营养物浓度。因此,该方法可用于测量来自多种肿瘤类型的间质液以及大多数其他生物液中许多代谢产物的绝对浓度,从而可以表征实体瘤微环境中的营养水平。
【背景】细胞分裂需要在分裂前复制母细胞的生物量。结果,生长中的细胞必须能够利用其环境中可用的营养素来合成分裂所需的大分子。为了维持癌性增生,肿瘤通常表现出新陈代谢的改变(DeBerardinis和Chandel,2016)。在许多情况下,肿瘤的代谢是由细胞内在的过程(如致癌激活)驱动的(Cairns等,2011; Nagarajan等,2016)。但是,最近的工作强调了细胞外在因子在决定癌细胞代谢中的重要性(Anastasiou,2017; Bi et al。,2018; Muir et。,2018 )。细胞外环境在塑造癌症代谢中的重要性也许不足为奇,因为癌细胞所处的营养环境限制了该细胞内可能发生的代谢反应。由于细胞外在的代谢物水平可以在确定肿瘤细胞的行为中发挥作用,因此在生理条件下检查肿瘤细胞的代谢至关重要。但是,我们对肿瘤微环境的代谢组成缺乏了解。

癌细胞可利用的营养环境主要由组织液组成(Wiig和Swartz,2012)。了解肿瘤间质液的营养成分将有助于洞悉肿瘤细胞因其环境而受到的代谢限制。存在多种从正常器官和肿瘤中分离组织液的方法(Wiig et al。,2010)。但是,早期无法测量多种代谢物的方法限制了组织间液营养成分的尝试,因此,我们在有限的动物肿瘤模型中对肿瘤中营养物质的利用仅限于少数代谢物(Burgess和Sylven, 1962年;古利诺(Gullino)等人(1964年)。质谱技术的出现允许同时检测许多代谢物。然而,尽管技术进步,但是代谢组学研究由于存在于生物体液中的成分可以抑制或增强特定代谢物的检测这一事实而变得复杂。这些在不同生物流体之间的代谢物检测中的差异被称为“基质效应”,并且是比较不同生物流体之间的代谢物浓度以及准确定量这些流体中代谢物的主要混杂因素(Panuwet等人。,2016;沙利文(etull。),2019)。

在这里,我们演示了一种基于离心的肿瘤间质液分离方法,以及随后使用稳定同位素稀释法对该流体中众多代谢物进行绝对定量的方法,该技术是将感兴趣的代谢物的稳定同位素标记的内标添加到实验样品中。这些稳定的同位素内标与样品中相应的代谢物具有相同的基质效应,并且与样品中的代谢物相比,其质量增加了。为了同时测量许多代谢物,我们首先从酵母的极性代谢物提取物中定量 13 C代谢物的浓度,并用 13 C同位素标记的葡萄糖作为唯一碳进行培养资源。然后将这种定量的酵母提取物用作内标,可对生物样品中的目标代谢产物进行可靠的定量,同时最大程度地减少基质效应带来的系统误差。这种方法提供了一种定量生物流体中极性代谢物的可靠方法,并补充了类似的基于同位素稀释的现有方法,例如主要定量非代谢物的商业化Biocrates AbsoluteIDQ试剂盒(Gieger等人,2008年)。生物样品中的极性脂质。

通过该协议实现的代谢物水平的绝对定量可以直接比较各种肿瘤类型中的组织液成分,从而为系统地研究各种癌症和人类肿瘤样品的动物模型中的营养物提供了机会。此外,对组织液中代谢物水平的绝对定量可以生成模仿肿瘤中生理条件的组织培养基,从而扩大了体外 / 离体的范围可以在生理营养条件下进行的实验。最广泛地讲,该方案提供了一种方法,可以同时对复杂生物流体中的许多代谢物进行绝对定量,可用于研究任何生物材料的代谢成分。

关键字:营养物, 代谢组学, 微环境, 组织液, 肿瘤代谢, 质谱法

材料和试剂

  1. 50 ml锥形管(Falcon,目录号:14959 49A)
  2. 实验胶带(Thermo Fisher,目录号15-901-20H)
  3. EDTA涂层血浆收集管(Sarstedt,目录号:41.1395.105)
  4. 1 ml 25G TB注射器(Becton Dickinson,目录号:309625)
  5. 20μM网状尼龙过滤器(Spectrum Labs,目录号:148134)
  6. Whatman纸(Thermo Fisher,目录号:88600)
  7. 培养皿(Corning,目录号:07202011)
  8. Eppendorf管(Corning,目录号:14222168)
  9. LC / MS样品瓶(Thermo Fisher,目录号:C4000-11)
  10. LC / MS样品瓶盖(Thermo Fisher,货号:C5000-54B)
  11. 专用于LCMS的玻璃器皿(瓶,量筒,锥形瓶)(不需通过中央玻璃清洗服务进行清洗,因为这可能会将表面活性剂和其他污染物引入系统中)
  12. 移液器(Gilson,目录号:F167380)
  13. 鼠标手术包(Thermo Fisher,目录号:14516249)
  14. 血库盐水(Azer Scientific,目录号:16005-092)
  15. 70%乙醇(Thermo Fisher,目录号:04-355-305)
  16. 乙腈LC / MS Optima 4 L(Fisher Scientific,目录号:A955-4)
  17. 甲醇LC / MS Optima 4 L(Fisher Scientific,目录号:A456-4)
  18. 皮尔斯甲酸(99%,LC / MS等级,Life Technologies,目录号:28905)
  19. 水LC / MS Optima 4 L(Fisher Scientific,目录号:W64)
  20. 碳酸铵(Sigma-Aldrich,目录号:379999)
  21. 氢氧化铵溶液(28%,Sigma-Aldrich,目录号:338818)
  22. 代谢组学氨基酸标准混合物(剑桥同位素实验室,目录号:MSK-A2-1.2)
  23. 13 C同位素标记的酵母代谢产物提取物(Cambridge Isotope Laboratories,Inc.,目录号:ISO1)
  24. 乳酸 13 C 3 (Sigma-Aldrich,目录号:485926)
  25. 13 C 3 甘油(剑桥同位素实验室,目录号:CLM-1510)
  26. 13 C 6 15 N 2 胱氨酸(剑桥同位素实验室,目录号:CNLM-4244)
  27. 2 H 9 胆碱(剑桥同位素实验室,目录号:DLM-549)
  28. 13 C 4 3-羟基丁酸酯(剑桥同位素实验室,目录号:CLM-3853)
  29. 13 C 6 葡萄糖(剑桥同位素实验室,目录号:CLM-1396)
  30. 13 C 2 15 N牛磺酸(剑桥同位素实验室,目录号:CNLM-10253)
  31. 2 H 3 肌酐(剑桥同位素实验室,目录号:DLM-3653)
  32. 13 C 5 次黄嘌呤(剑桥同位素实验室,目录号:CLM-8042)
  33. 13 C 3 丝氨酸(剑桥同位素实验室,目录号:CLM-1574)
  34. 13 C 2 甘氨酸(剑桥同位素实验室,目录号:CLM-1017)
  35. 化学标准品库1
    class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:400px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    丙氨酸
    西格玛
    A7469
    精氨酸
    西格玛
    A6969
    天冬酰胺
    西格玛
    A7094
    天冬氨酸
    西格玛
    A7219
    肉碱
    西格玛
    C0283
    瓜氨酸
    西格玛
    C7629
    胱氨酸
    西格玛
    C7602
    谷氨酸
    西格玛
    G8415
    谷氨酰胺
    西格玛
    G3126
    甘氨酸
    西格玛
    G7126
    组氨酸
    西格玛
    53319
    羟脯氨酸
    西格玛
    H54409
    异亮氨酸
    西格玛
    I7403
    亮氨酸
    西格玛
    L8912
    赖氨酸
    西格玛
    L8662
    蛋氨酸
    西格玛
    M5308
    鸟氨酸
    西格玛
    75469
    苯丙氨酸
    西格玛
    P5482
    脯氨酸
    西格玛
    P5607
    丝氨酸
    西格玛
    S4311
    牛磺酸
    西格玛
    T0625
    苏氨酸
    西格玛
    T8441
    色氨酸
    西格玛
    T8941
    酪氨酸
    西格玛
    T8566
    缬氨酸
    西格玛
    V0513
    乳酸
    西格玛
    L7022
    葡萄糖
    西格玛
    G7528
  36. 化学标准品库2
    class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:450px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    2-羟基丁酸
    西格玛
    220116
    2-氨基丁酸
    西格玛
    162663-25G
    AMP
    西格玛
    A1752
    精氨酸琥珀酸酯
    西格玛
    A5707-50MG
    甜菜碱
    西格玛
    61962
    生物素
    西格玛
    B4639
    肌肽
    西格玛
    C9625-5G
    胆碱
    西格玛
    C7017
    CMP
    西格玛
    C1006
    肌酸
    西格玛
    C0780-50G
    胞苷
    西格玛
    C4654
    dTMP
    西格玛
    T7004-100MG
    果糖
    西格玛
    F0127
    1-磷酸葡萄糖
    西格玛
    G6750
    谷胱甘肽
    西格玛
    G4251
    GMP
    西格玛
    G8377
    IMP
    西格玛
    57510-5G
    邻磷酸乙醇胺
    西格玛
    P0503-1G
    吡rid醛
    西格玛
    P9130-500MG
    硫胺素
    西格玛
    T4625
    反-尿烷酸酯
    开曼
    16228
    UMP
    西格玛
    U6375-1G
    黄嘌呤
    西格玛
    X7375-10G
  37. 化学标准品库3
    class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:500px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    3-羟基丁酸
    西格玛
    H6501
    乙酰丙氨酸
    西格玛
    A4625-1G
    乙酰丙酮酯
    西格玛
    00920-5G
    乙酰肉碱
    西格玛
    A6706
    乙酰谷氨酰胺
    西格玛
    A9125-25G
    ADP
    西格玛
    A5285
    尿囊素
    西格玛
    05670-25G
    CDP
    Abcam
    ab146214-100毫克
    CDP-胆碱
    阿尔法
    J64161-10 g
    辅酶A
    西格玛
    C4282
    肌酐
    西格玛
    C4255-10MG
    γ-氨基丁酸
    西格玛
    A2129-10G
    GDP
    西格玛
    G7127
    谷胱甘肽二硫化物
    西格玛
    G4376
    甘油
    西格玛
    367494
    次黄嘌呤
    西格玛
    H9377
    肌醇
    西格玛
    I5125
    NAD +
    西格玛
    N1511
    对氨基苯甲酸酯
    西格玛
    A9878
    磷酸胆碱
    西格玛
    P0378-5G
    山梨糖醇
    西格玛
    W302902
    UDP
    西格玛
    94330-100MG
    UDP-葡萄糖
    西格玛
    U4625-100MG
  38. 化学标准品库4
    class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:500px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    苯乙酰谷氨酰胺
    开曼
    16724-25mg
    乙酰谷氨酸
    西格玛
    855642
    乙酰甘氨酸
    西格玛
    A16300-5G
    乙酰蛋氨酸
    西格玛
    01310-5G
    不对称二甲基精氨酸
    开曼
    80230
    ATP
    西格玛
    A2383
    CTP
    西格玛
    C1506
    dATP
    西格玛
    D6500
    dCTP
    西格玛
    D4635
    脱氧胞苷
    西格玛
    D3897
    叶酸
    西格玛
    F8758
    GTP
    西格玛
    G8877
    下丘脑
    西格玛
    H1384-100MG
    蛋氨酸亚砜
    西格玛
    M1126-1G
    甲基硫代腺苷
    西格玛
    D5011-25MG
    磷酸肌酸
    西格玛
    P7936-1G
    吡rid醇
    西格玛
    P9755
    5-磷酸核糖
    西格玛
    83875
    SAH
    西格玛
    A9384-25MG
    胸苷
    西格玛
    T9250
    三甲基赖氨酸
    西格玛
    T1660-25MG
    尿苷
    西格玛
    T1660-25MG
    尿苷
    西格玛
    U3003
    UTP
    西格玛
    U6625
  39. 化学标准品库5
    class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:500px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    3-磷酸甘油酯
    西格玛
    P8877
    顺式乌头酸
    西格玛
    A3412-1G
    柠檬酸盐
    西格玛
    754
    DHAP
    西格玛
    51269
    1,6-二磷酸果糖
    西格玛
    F6803
    富马酸酯
    西格玛
    240745
    6-磷酸葡萄糖
    西格玛
    G7879
    3-磷酸甘油
    开曼
    20729-100毫克
    胍基乙酸酯
    西格玛
    G11608
    Kynurenine
    西格玛
    K8625
    苹果酸
    西格玛
    2288
    NADP +
    西格玛
    N0505
    烟酰胺
    西格玛
    72340
    2-草酸戊二酸酯
    西格玛
    75890-25G
    磷酸烯醇式丙酮酸
    西格玛
    P3637
    丙酮酸
    西格玛
    P5280
    琥珀酸酯
    西格玛
    S3674
    尿嘧啶
    西格玛
    U0750
  40. 化学标准品库6 class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:500px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    3-羟基异丁酸
    Adipogen
    CDX-H0085-M250
    2-羟基戊二酸
    西格玛
    H8378
    氨基己二酸酯
    西格玛
    A0637
    β-丙氨酸
    西格玛
    14064
    氨基甲酸酯类
    阿尔法
    A17166-10克
    半胱氨酸
    开曼
    16061-50毫克
    半胱氨酸
    圣克鲁斯
    sc-485621
    FAD
    西格玛
    F6625
    甘油磷酸胆碱
    西格玛
    G5291-50MG
    肌苷
    西格玛
    I4125
    旋转
    西格玛
    O2875
    泛酸
    西格玛
    P5155
    磷酸丝氨酸
    Fluka
    79710
    核黄素
    西格玛
    R9504
    UDP-GlcNAc
    西格玛
    U4375
    尿酸
    西格玛
    U2625-25G
  41. 化学标准库7
    class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:500px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> <身体>代谢物名称
    制造商
    部件号
    衣康酸
    西格玛
    I29204
    同型半胱氨酸
    TCI
    H0159
    2-氧代丁酸
    西格玛
    K401
    2-羟基丁酸
    西格玛
    220116
    抗坏血酸
    西格玛
    A7506
    肌氨酸
    西格玛
    131776-100G
    二甲基甘氨酸
    西格玛
    D1156-5G
    N6-乙酰赖氨酸
    西格玛
    A4021-1G
    Pipecolate
    西格玛
    P45850-25G
    吲哚乳酸酯
    西格玛
    I5508-250MG-A
    吡啶甲酸
    西格玛
    P42800-5G
    3-甲基-2-氧代丁酸酯
    西格玛
    198994-5G
    3-甲基-2-氧戊酸
    西格玛
    198978-5G
    甲酰蛋氨酸
    西格玛
    F3377-250MG
    2-氨基丁酸
    西格玛
    A2536-1G
    同瓜氨酸
    圣克鲁斯
    sc-269298-100毫克
    γ-谷氨酰丙氨酸
    西格玛
    483834-500MG
    甘露糖
    西格玛
    M6020-25G
    半胱氨酸-甘氨酸(二肽)
    西格玛
    C0166-100MG
  42. 流动相A(请参阅食谱)
  43. 流动相B和针洗(请参见食谱)
  44. 后密封件清洗(请参阅配方)
  45. 80%的甲醇含有 13 C- 15 N标记的氨基酸混合物(请参见食谱)
  46. 具有同位素标记的内标(EB)的提取缓冲液(请参见食谱)
  47. 化学标准文库的准备工作(请参见食谱)

设备

  1. Sorvall Legend X1R冷冻离心机(Thermo Fisher,目录号:75004260)
  2. Sorvall Legend Micro 21冷冻离心机(Thermo Fisher,目录号:75002447)
  3. 搅拌机(Retsch,目录号:MM301)
  4. 50 ml混合罐(Retsch,目录号:01.462.0216)
  5. 5 mM研磨球(Retsch,目录号:05.368.0034)
  6. LP涡旋混合器(Thermo Fisher,目录号:11676331)
  7. 分析天平(梅特勒-托利多,目录号:AL54)
  8. Dionex Ultimate 3000 UHPLC配备RS Binary Pump,RS柱温箱和RS自动进样器(Thermo Fisher Scientific,San Jose,CA)
  9. QExactive混合四极杆Orbitrap台式质谱仪,配备离子最大离子源和HESI-II探针(Thermo Fisher Scientific,加利福尼亚州圣何塞)
  10. SeQuant ? ZIC ? -pHILIC 5 µm 150 x 2.1 mm聚合分析型PEEK HPLC色谱柱(Millipore Sigma,目录号:1504600001)
  11. SeQuant ? ZIC ? -pHILIC 5ŚĚm20 x 2.1 mm PEEK保护套件,带耦合器(3 pc)(Millipore Sigma,目录号:15043800001)
软件 1. Thermo Scientific Xcalibur 4.1 SP1(Thermo Fisher Scientific) 2. Microsoft Excel 程序 1. 学习规划 1. 推荐多少生物复制品? 1. 研究所需的生物学复制品的数量将取决于样品之间的变异性。对于动物研究,可以控制大量变量(即,肿瘤遗传学,肿瘤大小,动物遗传学,动物饮食,间质液分离时间),变异性可能会小于人类样品。此外,所需的重复次数将取决于要进行的实验的预期目的。例如,如果预期目的是确定两种肿瘤类型之间的组织液中是否存在营养差异,则除了确定样本之间的变异性之外,确定组之间的效应大小也很重要,以便估算所需的样本大小。我们建议生成试点数据或利用先前发布的有关TIF成分差异的数据(Sullivan 等,2019)估计效应大小,并利用Metaboanalyst中的功效分析软件(Chong 等。(2018年)或其他统计分析软件来估算所需的生物复制次数。 2. 另外,在确定所需的生物学复制数时,重要的是要注意,并非每个肿瘤都必定会产生组织液。根据我们的经验,大约75%的小鼠胰腺腺癌产生的肿瘤间质液(TIF)的体积为5至180μl(Sullivan 等人,2019)。因此,可能需要其他样本才能达到从功率分析确定的所需重复次数。 2. 我们建议同时从所有参与研究的动物中采集TIF。昼夜节律和食物摄入会改变血浆代谢物水平,从而改变TIF代谢物水平,从而带来额外的变异性(Dallmann 等,2012; Abbondante 等,2016)。如果必须在不同的日子从动物身上采集TIF,我们建议在一天中的同一时间采集TIF。 3. 我们建议两个人一起工作以分离TIF和心脏血液,以提高TIF采集的速度,以防止由于安乐死和TIF采集之间的缺血时间延长而导致TIF组成发生变化。在我们自己的实验中,解剖在约2分钟内完成。并且我们发现了缺血改变肿瘤代谢物水平的有限证据(Sullivan et al。,2019)。 4. 我们已使用程序B中所述的方案,成功地从乳腺癌,肺癌,前列腺癌,胰腺癌和黑色素瘤的多种基因工程和植入小鼠模型中分离出了TIF,其他人已经从小鼠黑色素瘤和乳腺癌模型中分离出了TIF(Ho 等, 2015; Eil等人,2016; Spinelli 等人,2017; Zhang 等人,2017)使用类似的协议。另外,已经使用类似的方案从肾脏(Siska 等人,2017)和卵巢癌(Haslene-Hox 等人,2011)中分离TIF 。因此,我们预计TIF分离方案可以成功用于多种类型的小鼠和人类肿瘤。 5. 程序C中所述的分析使用了(Sullivan 等人,2019)中所述的7个独立的化学标准品库,可对通常在生物流体中测量的149种代谢物进行定量分析(Lawton 等人,2008; Evans 等人,2009; Mazzone 等人,2016; Cantor 等人,2017)。但是,根据实验目标,可能不需要使用所有7个标准池的完整分析。如果不需要完整的分析,则可以使用化学标准库的子集来覆盖感兴趣的分析物。请注意,尽管已仔细选择了该方案提供的标准库中的单个代谢物,以避免相同m / z(异构体和同量异构体)的代谢物位于同一池中。另外,可以通过源内裂解从较大的代谢物生成的代谢物已经分离。 6. 该方案中对液相色谱-质谱分析的描述(程序D)是有经验的仪器操作人员执行所述分析的粗略指南。成功的样品质谱分析需要训练有素的UHPLC和Thermo Scientific混合四极杆-Oribtrap质谱仪操作员。 2. 从荷瘤动物中分离TIF和血浆 1. 准备一个TIF隔离管(图1 A)。 1. 取一个尼龙过滤器,并将其放在50毫升锥形管的顶部。 2. 用实验室胶带将过滤器粘下来。确保将过滤器稍微松散地固定在锥形管的顶部,以使肿瘤可以将过滤器稍微向下推入锥形管中。 图1.使用附着在锥形管上的尼龙网过滤器收集TIF。答:使用实验室胶带将过滤器松散地固定在锥形管的顶部,这样当将样品放在过滤器顶部时,过滤器会轻微下垂到锥形管中。B.在过滤器和锥形管顶部的样品。C.将样品添加到过滤器中后,将锥形管的盖子放在顶部,但不要拧到锥形上。而是使用实验室胶带将其固定在适当的位置。D.离心后,在锥形管中收集约30μl的肿瘤组织液(此处为蓝色,以示对比)。 2. 事先准备材料,以便快速解剖小鼠。 1. 将预冷的(4°C)盐水(〜25 ml)放入培养皿中以清洗肿瘤。 2. 用生理盐水冲洗后,用一块约4平方厘米的Whatman纸干燥肿瘤。 3. 准备一个25G TB注射器以备收集心脏血液。 4. 在解剖小鼠之前,在冰上标记和预冷一根EDTA包被的血浆收集管10分钟。 3. 通过颈脱位使小鼠安乐死。 4. 在切口部位周围喷洒70%的乙醇,以防止头发沾染样品。 5. 快速,干净地将肿瘤从动物身上切除。解剖的确切程序将取决于肿瘤的解剖位置。一个人应该继续执行以下步骤,而另一个人可以开始进行血浆分离(步骤B6)。 1. 将肿瘤放入含盐水的培养皿中冲洗肿瘤。 2. 在Whatman纸上将肿瘤吸干。在此步骤中要小心,以确保所有的盐水都从肿瘤中吸出,以使盐水不会污染TIF。 如果担心TIF分离过程中的盐污染,我们建议执行以下附加步骤:向盐溶液中添加非天然代谢产物,如去甲缬氨酸。照常继续执行协议的其余部分。随后,在分析TIF样品时,确定TIF样品中是否存在非天然代谢物。如果检测到非天然代谢物,则表明盐污染,而如果未检测到代谢物,则盐污染的可能性很小。 3. 可选:如果需要此信息,请称量肿瘤。如果需要,还可以通过卡尺测量来确定肿瘤体积。 4. 将肿瘤放在固定在锥形管上的尼龙网状过滤器上(图1B)。将50 ml锥形盖放在肿瘤上,并用胶带将盖固定到位(图1C)。 5. 使用冷藏的临床离心机(例如,Sorvall Legend X1R) 在4°C下以106 x g离心肿瘤10分钟。 6. 取下50 ml锥形管。如果隔离有效,则锥形管底部将有10-50μl的流体(图1D)。保持管在冰上。 注意:已发现人类肿瘤每克肿瘤产生5-150μl的液体(Haslene-Hox等,2011)。同样,我们从重约0.3-2.5 g的小鼠胰腺肿瘤中分离出10-50μl液体(Sullivan等人,2019)。肿瘤大小与TIF体积之间没有确切的相关性,因为许多因素可能会影响间质体积。但是,较大的肿瘤更有可能产生较大体积的TIF。充满大量液体的囊肿的肿瘤每克肿瘤可产生数百微升的液体。从分析中忽略这些,因为尚不清楚囊性液是否代表间质液。 7. 将隔离液移至新的Eppendorf管中。可以直接提取流体进行LC / MS分析,也可以将其冷冻并储存在-80°C以便将来分析。 我们已经在-80°C下保存2个月之前和之后分析了TIF样品(避免了冻融循环),并在保存之后检测到了相似的代谢产物浓度(Sullivan 等人,2019)。因此,在分析之前,TIF样品可以保存至少2个月,而无需进行冻融循环。 8. 分离出TIF的肿瘤可使用其他合适的方案进行其他分析。 注意:我们已经成功地使用了从中分离出TIF的肿瘤进行免疫组织化学,免疫印迹和流式细胞仪分析。 6. 使用25G TB注射器通过心脏穿刺从小鼠心脏分离血液。心脏穿刺可能是一项难以执行的技术。先前已经发布了带有心脏穿刺血液隔离视频文档的详细协议(Schroeder,2019)。如果不熟悉此技术,我们建议您利用这些资源获取有关以这种方式分离血液的更多详细信息。 1. 解剖开胸腔。 2. 将注射器插入心室。 3. 缓慢地抽血以防止心脏衰竭。 4. 从注射器中拔出针头以防止细胞裂解时从注射器中排出细胞。 5. 将血液排入EDTA涂层血浆收集管。 6. 保持血浆在冰上。 7. 在台式离心机(例如 Sorvall Legend Micro 21)中 以845 xg旋转EDTA涂层的血浆收集管,在4°C下旋转15分钟。 8. 从沉淀的血细胞中除去血浆,然后放入新鲜的Eppendorf管中。 9. 直接提取血浆用于LC / MS分析或冷冻并保存在-80°C以便将来分析。 注意:先前的研究发现血浆样品可以在-80°C(无冻融循环)下保存长达30个月,而许多代谢物的水平没有明显改变(Stevens 等人,2019)。因此,血浆样品可以在分析之前保存多个月。 3. 从TIF和血浆中提取代谢物 1. 准备合并的化学标准品库,其中包括要定量的代谢物。有关如何组合文库的详细信息,请参见``食谱''部分和表5-11(Sullivan 等人,2019)。 2. 准备具有适当同位素标记的内标的代谢物提取缓冲液(EB)(配方5)。如(Sullivan et al。,2019)中所述,有关使用同位素标准制备EB的详细信息,请参见``食谱''部分。 注意:为您拥有的样品和标准品数量加上足够的EB,再加上10%,以免在从每个样品中提取代谢物之前用完EB。每个样品和标准池稀释液需要45μlEB。EB中同位素标记的代谢物标准品不是无限稳定的。在每次实验前准备新鲜的EB。 3. 在HPLC级水中准备化学标准库的稀释液。最高浓度应为5 mM。 4. 接下来,按照以下步骤C5所述,在HPLC级水中制成每个标准液的稀释系列。提取前将它们放在冰上。请注意,要建立将已知代谢物浓度与LC / MS响应相关的标准曲线,需要对化学标准库进行多种稀释,这对于下游分析代谢物浓度是必需的。下面我们建议一种方案,以生成涵盖代谢物生理浓度的8点标准曲线,但可能会有变化。 5. 对于每个池,5 mM池并不总是存在于溶液中,因此请剧烈涡旋并立即从该混合物中移液,以防止错误沉淀颗粒: 1. 取20μl5 mM储备液,并稀释到80μlHPLC级水中,得到1 mM溶液。 2. 取30μl1 mM储备液,并稀释到70μlHPLC级水中,得到300μM溶液。 3. 取33.33μl的300μM储备液,并稀释到66.67μlHPLC级水中,得到100μM溶液。 4. 取30μl100μM的储备液,并稀释到70μlHPLC级水中,得到30μM的溶液。 5. 取33.33μl的30μM储备液,并稀释到66.67μlHPLC级水中,得到10μM溶液。 6. 取30μl的10μM储备液,并稀释到70μlHPLC级水中,得到3μM溶液。 7. 取33.33μl的3μM储备液,并稀释到66.67μlHPLC级水中,得到1μM溶液。 6. 在冰上解冻TIF和血浆样品。 7. 将5μl每个TIF样品,血浆样品和化学标准品稀释液(配方6)添加到新的Eppendorf管中。保持冰上。 8. 向每个样品/标准液中加入45μlEB。保持冰上。 9. 在4°C下以最大速度涡旋所有样品10分钟。 10. 在4°C下 以21,000 xg旋转所有样品10分钟。 11. 从Eppendorf管中取出20μl混合物,并添加到LC / MS样品瓶中。盖上小瓶。 注意:LC / MS样品瓶中至少需要15μl,以确保自动进样器正确正确地进样。材料和试剂中描述的小瓶包含熔融插入物。没有插入物的小瓶将需要更大的体积。 12. 将剩余的样品冷冻在Eppendorf管中,并储存在-80°C下。如果初始LC / MS不成功,则可以稍后运行此示例。 4. 提取代谢物的液相色谱-质谱分析 1. 首先使用清洁的系统(在您的实验室中使用适当的LC清洁方法)。 2. 计算所需的流动相A(配方1)的量,并在分析当天进行新鲜准备。存放不超过一周。 注意:根据您的系统,每次注射将使用〜2 ml,并且瓶中还需要50-100 ml。不要忘记为其他进样类型(例如溶剂空白样和系统适用性测试)做足够的流动相A,除了样品外还必须运行。 3. 计算所需的流动相B(配方2)的量,并根据需要准备更多的流动相。根据您的系统,每次注射将需要〜2 ml,另外每瓶需要50-100 ml。 4. 检查后密封垫清洗液的水平(配方3),并根据需要加水。 5. 如果使用的UHPLC系统带有单独的针头清洗器,请在其中填充乙腈。 6. 连接SeQuant ?ZIC ?-pHILIC 5μm150 x 2.1 mm分析柱,通过Guard试剂盒中提供的连接器连接到Guard柱。 7. 使用标准技术将色谱柱和保护柱连接到UHPLC系统。 8. 将柱箱温度设置为25°C。 9. 将自动进样器温度设置为4°C。 10. 设置初始条件:在80%B的情况下将流速设置为0.150 ml / min。记录初始压力值。 注意:ZIC-pHILIC色谱柱不能像常规反相色谱柱那样承受如此高的背压和进样量。注意背压,不要超过制造商建议的最大压力。最好将方法中的最大压力设置为低于制造商设定的最大压力,以免损坏色谱柱。 11. 在系统上运行任何操作之前,请先将色谱柱与起始条件(80%B)平衡30分钟。 12. 检查质谱仪上的质量校准。如果质量在上周内未进行校准,或者质量检查未通过,请使用制造商建议的标准校准混合物进行重新校准。此外,通过将甘氨酸和天冬氨酸加标到校准混合物中,或按照制造商的建议,执行自定义的低质量校准。 13. 通过运行系统适应性测试,例如将氨基酸混合物注入色谱柱和MS中,确保您的整个LCMS系统性能都可以接受。检查信号强度以及峰形和分离度。 14. 对于UHPLC梯度,请使用下表1所示的条件: 表1. LC参数 时间(分钟) 流量(ml / min) %B 0.00 0.150 80 20.0 0.150 20 20.5 0.150 80 28.0 0.150 80 15. 16. 在全扫描,极性切换模式下操作质谱仪,扫描范围为70-1000 m / z。以负模方式包括从220到700 m / z的附加窄范围扫描,以改善核苷酸检测。使用表2和3中显示的以下参数作为MS: 表2。MS源参数 参数 设置 喷涂电压 3.0 kV(正); 3.1 kV(负) 加热毛细管 275°摄氏度 HESI探针 350°C 鞘气 40单位 辅助气体 15伙 扫气 1个 17. 表3. MS扫描参数 参数 设置 解析度 70,000 AGC目标 1E6 最大IT 20毫秒 18. 19. 请注意,在此特定研究中,我们之前已收集了每种要定量的代谢物的MS / MS数据,并使用化学标准品库将其用于确认保留时间。如果将新的代谢物添加到标准池中,请收集有关标准本身以及合并的生物样品的MS / MS数据,以帮助确认峰鉴定。 20. 使用Thermo Scientific Xcalibur序列设置视图编写序列(样品运行顺序)。 注意:由于此方法中使用的样品量极少,因此该序列与典型序列有所不同,后者将包括色谱柱进样以及质量控制合并样品。由于运行了多个标准曲线,并且每个样品都包含13 C标记的内标物,因此我们选择放弃使用珍贵的样品来创建QC库,而是使用标准曲线和13 C内标物确定代谢物检测的线性和一致性。 1. 首先注入几个空白的水,以确保系统清洁无残留物和污染物。 2. 使用用于制备萃取混合物的75/25 / 0.1乙腈/甲醇/甲酸混合物包括溶剂空白。 3. 然后进行系统适应性测试(SST)注入。我们使用含有13 C- 15 N标记氨基酸混合物的80%甲醇(配方4)。 4. 将样品添加到您的序列中,并遵循标准曲线,从最低浓度开始直到最高浓度达到每条曲线。用溶剂空白物分开每条曲线,并检查是否有残留。 5. 每8-10个样品插入额外的SST进样。这些将用作QC,以确保不会随时间流逝丢失信号。 6. 将每种进样类型的进样量设置为2μl。 7. 将仪器方法设置为适当的方法。 8. 保存序列文件。 9. 随机化样品运行顺序,以减少随时间流逝信号丢失的机会。将序列导出为.csv文件。在Microsoft Excel中打开,将样品剪切并粘贴到新选项卡中,留下空白,STT和标准曲线。添加另一列,并使用= rand()函数为每个样本创建随机数。现在,使用随机数值从最小到最大对样本进行排序。剪切并粘贴回到包含空白,SST和标准曲线的先前序列中。保存.csv文件,文件名中带有“ random”。将新的随机序列导入Xcalibur Sequence View,并使用文件名中的“ random”进行保存。 10. 根据顺序将样品瓶放在自动进样器中样品瓶位置。使用非随机序列检查样品的样品瓶位置。 11. 确保溶剂空白样品瓶和SST样品瓶包含足够的体积以进行多次进样。 21. 运行序列。 注意:有关TIF和血浆样品的LC / MS分析的预期输出示例,可从https://www.metabolomicsworkbench.org/data/DRCCMetadata.php获得(Sullivan等人,2019)的LC / MS数据。?Mode = Project&ProjectID = PR000750 。 数据分析 1. 识别代谢物峰。该协议将描述Thermo Scientific Xcalibur中的峰鉴定,但可以与任何其他峰鉴定方法一起使用。 1. 生成一个处理方法文件,该文件将用于识别每种目标代谢物的峰: 1. 创建一种新的处理方法。 2. 加载一个.raw文件,其中包含来自包含所关注代谢物的外标样品以及该代谢物的13 C内标的LC / MS数据。 注意:通常,使用标准曲线中间的外部标准样品效果最佳;有时标准曲线上的高浓度点具有较差的质量峰。 3. 计算目标代谢物的确切质量。 笔记: 1. 精确质量是通过将化合物中每种元素的最丰富同位素的质量相加来确定的。例如,CO 2的精确质量是碳12原子(12.000)+两个氧16原子(15.995 + 15.995)的总质量= 43.990。化合物的确切质量将不同于其分子量;元素的分子量是通过对该元素的每个同位素的质量取平均值,然后按自然界中每个同位素的丰度来加权的。 2. 如果使用Thermo Scientific Xcalibur,请使用QualBrowser模块中的“同位素模拟”选项卡计算精确质量。输入化学式,确保未选中“加合物”复选框,然后选择“新建”。确保将软件全局设置设置为5 ppm的质量公差,并将质量精度设置为5个小数位。理想情况下,您使用的峰积分软件应为您计算出该值。 4. 计算正和负电离模式下目标代谢物的质荷比(m / z)。 笔记: 1. 对于小极性代谢物的分析,最常见的离子将是获得或丢失单个质子的离子,并且大多数分子的电荷状态为1。 2. 如果使用Thermo Scientific Xcalibur,请通过输入公式,选中“加合物”框并选择+1或-1电荷来确定质量,使用QualBrowser模块中的同位素模拟器计算m / z。 / z分别处于正向或负向模式。 3. 有多种在线工具可提供准确的质量信息,并计算各种不同加合物的m / z。最全面的是Metlin数据库(Guijas等,2018):https ://metlin.scripps.edu/landing_page.php?pgcontent=mainPage 。 5. 在处理方法中,根据要搜索带正电的离子还是带负电的离子,选择正电离模式或负电离模式。 注意:某些代谢物更容易以阳性或阴性模式检测。(Sullivan等人,2019)的补充文件1中列出了用于各种代谢物的哪种模式的建议列表。如果没有可用的建议,请通过尝试两种方法凭经验确定哪种方法可以提供更好的检测。 6. 确定并验证每种代谢物的保留时间: 1. 搜索感兴趣离子的精确质量。 2. 请注意,与目标离子的精确质量相匹配的所有峰的保留时间均在5 ppm之内。 3. 打开其他外部标准样品的.raw文件,其中感兴趣的代谢物浓度较低。 1) 请注意,与目标离子的精确质量匹配的峰面积会减小。 2) 对每个包含目标代谢物的外标样品重复上述步骤,检查哪个峰面积跟踪了预期的代谢物量。 3) 请参考MS / MS数据以确认峰鉴定。 4. 打开一个不包含目标代谢物的.raw文件。确保此.raw文件中不存在任何候选峰。 5. 搜索感兴趣离子的13 C标记版本的精确质量。 注意:该峰在所有样品中的面积均应大致相同。 6. 检查13 C标记的标准峰的保留时间是否与候选峰的保留时间完全匹配。 7. 对所有感兴趣的代谢物重复上述步骤。 7. 将13 C标记的标准品指定为其相应12 C代谢物的内部标准品。 对于没有13 C内标的代谢物,应指定保留时间与内标相似的13 C代谢物。 2. 使用处理方法来挑选和验证所有LC / MS数据文件(实验样品和外标)中所有代谢物的峰: 1. 一旦自动选择了所有峰,请手动检查每种代谢物和每个样品的每个峰,以确保正确鉴定了所有峰。自动峰值选择算法出现的一些常见错误示例: 1. 选择了错误的峰:这可能发生在具有相似保留时间的同量异位化合物上,例如亮氨酸和异亮氨酸。 2. 从基线到基线未完全选择峰。 3. 选择峰,但与第二个峰重叠:在生物样品具有外标中不存在的重叠峰的情况下,偶尔会发生这种情况。如果是这种情况,则不应使用此LC / MS方法对这种代谢物进行定量,而应确定另一种色谱分离方法。 2. 将样品相对于13 C内标的峰面积比导出到Microsoft Excel或您选择的数据处理软件。 2. 确定外标中相对峰面积与代谢物浓度之间的关系。 1. 根据称量的量计算外标曲线上每个点的每种代谢物的确切浓度。 2. 生成每个外标样品中代谢物浓度与代谢物相对峰面积的图。 3. 检查代谢物的相对峰面积是否随浓度线性增加: 1. 将线性回归拟合到图形。 2. 线性回归 的R 2值应≥0.995。 代谢物通常在高浓度时非线性响应。如果标准曲线上的点比实验样品中的浓度高得多,则可以去除标准曲线上的最高点。只要确保所有样品的相对峰面积都在标准曲线的线性范围内即可。 3. 非线性代谢物应从定量分析中排除,因为这种线性度的缺乏会妨碍同位素稀释法进行准确定量。 3. 确定添加到所有样品中的内标浓度。使用以下关系式 求解每个外标样品中存在的13 C内标物的浓度: 注意:此关系可用于计算每个外标样品中13 C内标物的浓度;每个中应存在相同的浓度。为了获得13 C内标物浓度的最准确值,请对与实验样品浓度最相似的外标点浓度求平均值。 4. 通过同位素稀释计算实验样品中每种代谢物的浓度。使用步骤C中定义的相同关系求解12 C代谢物的浓度。 5. 使用外标曲线计算所有其他分析物的半定量浓度。这些值被认为是半定量的,因为它们受到与生物样品进行比较而产生的基质效应的影响,而生物样品与溶于水的外标比较。这些基质效应可能是实质性的(Sullivan et al。,2019)。 1. 计算在步骤B3a中计算的线性回归的斜率和截距。 2. 使用该斜率和截距计算近似于代谢物浓度的半定量值。 3. 手动评估从此计算得出的浓度: 1. 在不含该代谢物的外标样品中,检查每种代谢物的值是否为零。 2. 任何显示负浓度的数据都应删除。 6. 使用Metaboanalyst(https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml)(Chong et al。,2018)或其他统计分析程序对数据进行统计分析。 1. 自动缩放数据(均值中心并除以每个浓度的标准偏差)。 2. 为了广泛比较两种样品类型中代谢物是否存在差异,请执行主成分分析或层次聚类。 3. 为了鉴定样品类型之间浓度不同的特定代谢物,生成一个火山图,其中原始P值为0.01,倍数变化为1.5,用于鉴定代谢物发生显着变化。 菜谱 1. 流动相A 20 mM碳酸铵 0.1%氢氧化铵(pH 9.4-9.6) Optima LC / MS水 2. 流动相B和针洗 最优LC / MS乙腈 3. 后密封垫清洗液 10%Optima LC / MS甲醇 Optima LC / MS水 4. 80%甲醇(含13 C- 15 N氨基酸标准混合物)(200毫升) 160毫升Optima LC / MS甲醇 40毫升Optima LC / MS水 40μl代谢组学氨基酸标准混合物 5. 具有同位素标记的内标(EB)的提取缓冲液(表4) 注意: 1. 这是配方中包含同位素标记的内标物,用于分析代谢产物,如(Sullivan等人,2019)。如果需要使用稳定的同位素内标对其他代谢物进行分析,请购买或合成所需的同位素标记的代谢物并将其添加到提取缓冲液中。当同位素标记的代谢物的丰度与待定量的未标记代谢物相似时,用同位素标记的标准品进行的定量效果最佳。因此,当添加同位素标记的内标物时,应添加同位素,使得当样品在提取缓冲液中稀释时,同位素的丰度与未标记的代谢物大致相似。 2. 该配方适用于180个样品,每个样品45μl,总体积为8,100μl。根据需要,对要分析的样品数量进行相应调整。添加所有成分后,短暂涡旋以确保EB充分混合,并在使用时存放在冰上。在每次实验前都要新鲜。切记在计算中包括额外的75/25 / 0.1乙腈/甲醇/甲酸作为溶剂空白。 表4.提取缓冲液(EB)的组成 零件 新增音量 最终浓度 HPLC级乙腈 5771.25微升 71.25% HPLC级甲醇 1923.75微升 23.75% HPLC级甲酸 15.39微升 1.9% 将约15 mg同位素标记的酵母提取物(ISO1)溶解在1.5 ml HPLC级水中。 注意:将水加到酵母提取物中后,通过将酵母提取物和水在4°C下涡旋和/或摇动约30分钟来溶解酵母提取物。溶液可以在-80°C下储存,尽管某些代谢物会随着时间而降解(请参阅制造商的说明) 405微升 5% 在HPLC级水中制备的 2 mM 2 H 9胆碱溶液(储存在-20°C) 4.03微升 1微米 在HPLC级水中制备的 50 mM 13 C 4 3-羟基丁酸酯溶液(储存在-20°C) 0.81微升 5微米 的200μM溶液13 Ç 6 15 Ñ 2胱氨酸在HPLC级水(储存在-20℃)制备 81微升 2微米 在HPLC级水中制备的 100 mM 13 C 3乳酸溶液(储存在-20°C) 16.2微升 200微米 在HPLC级水中制备的 57.3 mM 13 C 6葡萄糖溶液(储存在-20°C) 7.05微升 50微米 在HPLC级水中制备的 100 mM 13 C 3丝氨酸溶液(储存在-20°C) 1.62微升 20微米 在HPLC级水中制备的 750 mM 13 C 2甘氨酸溶液(储存在-20°C) 1.62微升 150微米 在HPLC级水中制备的 2 mM 13 C 5次黄嘌呤溶液(储存在-20°C) 2.02微升 0.5微米 在HPLC级水中制备的 200 mM 13 C 2 15N牛磺酸溶液(储存在-20°C) 2.02微升 50微米 在HPLC级水中制备的 60 mM 13 C 3甘油溶液(储存在-20°C) 2.02微升 15微米 在HPLC级水中制备的 4 mM 2 H 3肌酐溶液(储存在-20°C) 2.02微升 1微米 3. 6. 化学标准文库的制备 以下是(Sullivan 等人,2019)中描述的化学标准文库的制备方法。要准备这些化学文库,请从列出的供应商处购买化学药品,并按照指示进行称重,然后将每种代谢物放入50 ml混合磨罐中。使用Mixer Mill MM301将混合的代谢物与5个直径5 mm的不锈钢研磨球混合。在25 Hz的频率下进行1分钟混合的6个循环,然后静置3分钟。使用前,将现在混合的化学标准库粉末储藏在-20°C下。使用时,将每个混合化学文库重悬在HPLC级水中,浓度为5 mM,如下所示。 可以通过获取所需的纯化学标样并以等摩尔量混合纯化学标样来生成定制化学标样库。生成文库时,重要的是要确保每个文库都不会包含具有相同精确质量的代谢物,因为当混合到同一文库中时,不可能确定两种代谢物的正确保留时间。考虑将这些代谢物放入单独的合并库中(表5-11)。 7. 表5.化学标准品库1
8. 9. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:900px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 10. <身体> 11. 12.13. style =“ font-family:” font-size:12px; white-space:normal;“>代谢物名称
14. 15.16. 代谢物的分子量
17. 18.19. 分子量的化学标准
20. 21.22. 称量量(mg)
23. 24. 25.26.27. 丙氨酸
28. 29.30. 89.09
31. 32.33. 89.09
34. 35.36. 429.99
37. 38. 39.40.41. 精氨酸
42. 43.44. 174.2
45. 46.47. 210.66
48. 49.50. 1016.75
51. 52. 53.54.55. 天冬酰胺
56. 57.58. 132.12
59. 60.61. 150.13
62. 63.64. 724.60
65. 66. 67.68.69. 天冬氨酸
70. 71.72. 133.11
73. 74.75. 133.11
76. 77.78. 642.45
79. 80. 81.82.83. 肉碱
84. 85.86. 161.199
87. 88.89. 197.66
90. 91.92. 954.00
93. 94. 95.96.97. 瓜氨酸
98. 99.100. 175.2
101. 102.103. 175.2
104. 105.106. 845.60
107. 108. 109.110.111. 胱氨酸
112. 113.114. 240.3
115. 116.117. 240.3
118. 119.120. 1159.80
121. 122. 123.124.125. 谷氨酸盐
126. 127.128. 147.13
129. 130.131. 147.13
132. 133.134. 710.12
135. 136. 137.138.139. 谷氨酰胺
140. 141.142. 146.14
143. 144.145. 146.14
146. 147.148. 705.34
149. 150. 151.152.153. 甘氨酸
154. 155.156. 75.066
157. 158.159. 75.066
160. 161.162. 362.30
163. 164. 165.166.167. 组氨酸
168. 169.170. 155.1546
171. 172.173. 155.1546
174. 175.176. 748.85
177. 178. 179.180.181. 羟脯氨酸
182. 183.184. 131.13
185. 186.187. 131.13
188. 189.190. 632.89
191. 192. 193.194.195. 异亮氨酸
196. 197.198. 131.1729
199. 200.201. 131.1729
202. 203.204. 633.10
205. 206. 207.208.209. 亮氨酸
210. 211.212. 131.17
213. 214.215. 131.1729
216. 217.218. 633.10
219. 220. 221.222.223. 赖氨酸
224. 225.226. 146.19
227. 228.229. 182.65
230. 231.232. 881.56
233. 234. 235.236.237. 蛋氨酸
238. 239.240. 149.21
241. 242.243. 149.21
244. 245.246. 720.16
247. 248. 249.250.251. 鸟氨酸
252. 253.254. 132.16
255. 256.257. 168.62
258. 259.260. 813.84
261. 262. 263.264.265. 苯丙氨酸
266. 267.268. 165.19
269. 270.271. 165.19
272. 273.274. 797.28
275. 276. 277.278.279. 脯氨酸
280. 281.282. 115.13
283. 284.285. 115.13
286. 287.288. 555.67
289. 290. 291.292.293. 丝氨酸
294. 295.296. 105.09
297. 298.299. 105.09
300. 301.302. 507.21
303. 304. 305.306.307. 牛磺酸
308. 309.310. 125.15
311. 312.313. 125.15
314. 315.316. 604.03
317. 318. 319.320.321. 苏氨酸
322. 323.324. 119.119
325. 326.327. 119.119
328. 329.330. 574.92
331. 332. 333.334.335. 色氨酸
336. 337.338. 204.225
339. 340.341. 204.225
342. 343.344. 985.69
345. 346. 347.348.349. 酪氨酸
350. 351.352. 181.19
353. 354.355. 181.19
356. 357.358. 874.51
359. 360. 361.362.363. 缬氨酸
364. 365.366. 117.151
367. 368.369. 117.151
370. 371.372. 565.42
373. 374. 375.376.377. 乳酸
378. 379.380. 90.09
381. 382.383. 112.06
384. 385.386. 540.85
387. 388. 389. 390.391. 葡萄糖
392. 393.394. 180.1559
395. 396.397. 180.1559
398. 399.400. 869.52
401. 402. 403. 404. 405. 注意:对于5 mM溶液,以20.19 mg / ml的浓度溶解该溶液。
406.
407. 表6.化学标准品库2
408. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:900px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 409. <身体> 410. 411.412. 代谢物名称
413. 414.415. 代谢物的分子量
416. 417.418. 化学标准物的分子量
419. 420.421. 称量量(mg)
422. 423. 424.425.426. 2-羟基丁酸
427. 428.429. 104.1
430. 431.432. 126.09
433. 434.435. 12.61
436. 437. 438.439.440. 2-氨基丁酸
441. 442.443. 103.12
444. 445.446. 103.12
447. 448.449. 10.31
450. 451. 452.453.454. AMP
455. 456.457. 347.2212
458. 459.460. 347.22
461. 462.463. 34.72
464. 465. 466.467.468. 精氨酸琥珀酸酯
469. 470.471. 290.273
472. 473.474. 334.24
475. 476.477. 33.42
478. 479. 480.481.482. 甜菜碱
483. 484.485. 117.1463
486. 487.488. 117.15
489. 490.491. 11.71
492. 493. 494.495.496. 生物素
497. 498.499. 244.31
500. 501.502. 244.31
503. 504.505. 24.43
506. 507. 508.509.510. 肌肽
511. 512.513. 226.2324
514. 515.516. 226.23
517. 518.519. 22.62
520. 521. 522.523.524. 胆碱
525. 526.527. 104.1708
528. 529.530. 139.62
531. 532.533. 13.96
534. 535. 536.537.538. CMP
539. 540.541. 323.1965
542. 543.544. 367.16
545. 546.547. 36.72
548. 549. 550.551.552. 肌酸
553. 554.555. 131.133
556. 557.558. 131.13
559. 560.561. 13.11
562. 563. 564.565.566. 胞苷
567. 568.569. 243.2166
570. 571.572. 243.22
573. 574.575. 24.32
576. 577. 578.579.580. dTMP
581. 582.583. 320.1926
584. 585.586. 366.17
587. 588.589. 36.62
590. 591. 592.593.594. 果糖
595. 596.597. 180.16
598. 599.600. 180.16
601. 602.603. 18.02
604. 605. 606.607.608. 1-磷酸葡萄糖
609. 610.611. 260.135
612. 613.614. 336.32
615. 616.617. 33.63
618. 619. 620.621.622. 谷胱甘肽
623. 624.625. 307.3235
626. 627.628. 307.32
629. 630.631. 30.73
632. 633. 634.635.636. GMP
637. 638.639. 363.22
640. 641.642. 407.18
643. 644.645. 40.72
646. 647. 648.649.650. IMP
651. 652.653. 348.206
654. 655.656. 392.17
657. 658.659. 39.22
660. 661. 662.663.664. 邻磷酸乙醇胺
665. 666.667. 141.063
668. 669.670. 141.06
671. 672.673. 14.11
674. 675. 676.677.678. 吡rid醛
679. 680.681. 167.16
682. 683.684. 203.62
685. 686.687. 20.36
688. 689. 690.691.692. 硫胺素
693. 694.695. 265.35
696. 697.698. 337.23
699. 700.701. 33.72
702. 703. 704.705.706. 反-尿烷酸酯
707. 708.709. 137.118
710. 711.712. 137.12
713. 714.715. 13.71
716. 717. 718.719.720. UMP
721. 722.723. 324.1813
724. 725.726. 368.15
727. 728.729. 36.82
730. 731. 732. 733.734. 黄嘌呤
735. 736.737. 152.11
738. 739.740. 152.11
741. 742.743. 15.21
744. 745. 746. 747. 748. 注意:将溶液以28.54 mg / ml的浓度溶解5 mM溶液。
749.
750. 表7.化学标准品库3
751. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:700px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 752. <身体> 753. 754.755. 代谢物名称
756. 757.758. 分子量
759. 代谢物
760. 761.762. 分子量
763. 化学标准
764. 765.766. 称量量(mg)
767. 768. 769.770.771. 3-羟基丁酸
772. 773.774. 104.1045
775. 776.777. 126.09
778. 779.780. 252.18
781. 782. 783.784.785. 乙酰丙氨酸
786. 787.788. 131.1299
789. 790.791. 131.13
792. 793.794. 262.26
795. 796. 797.798.799. 乙酰丙酮酯
800. 801.802. 175.139
803. 804.805. 175.14
806. 807.808. 350.28
809. 810. 811.812.813. 乙酰肉碱
814. 815.816. 203.2356
817. 818.819. 239.70
820. 821.822. 479.40
823. 824. 825.826.827. 乙酰谷氨酰胺
828. 829.830. 188.183
831. 832.833. 188.18
834. 835.836. 376.36
837. 838. 839.840.841. ADP
842. 843.844. 427.203
845. 846.847. 501.32
848. 849.850. 1002.64
851. 852. 853.854.855. 尿囊素
856. 857.858. 158.121
859. 860.861. 158.121
862. 863.864. 316.24
865. 866. 867.868.869. CDP
870. 871.872. 403.177
873. 874.875. 403.20
876. 877.878. 806.40
879. 880. 881.882.883. CDP-胆碱
884. 885.886. 489.332
887. 888.889. 510.31
890. 891.892. 1020.62
893. 894. 895.896.897. 辅酶A
898. 899.900. 767.535
901. 902.903. 767.53
904. 905.906. 1535.06
907. 908. 909.910.911. 肌酐
912. 913.914. 113.12
915. 916.917. 113.12
918. 919.920. 226.24
921. 922. 923.924.925. γ-氨基丁酸
926. 927.928. 103.12
929. 930.931. 103.12
932. 933.934. 206.24
935. 936. 937.938.939. GDP
940. 941.942. 443.201
943. 944.945. 443.20
946. 947.948. 886.40
949. 950. 951.952.953. 谷胱甘肽二硫化物
954. 955.956. 612.631
957. 958.959. 612.63
960. 961.962. 1225.26
963. 964. 965.966.967. 甘油
968. 969.970. 106.0773
971. 972.973. 286.25
974. 975.976. 572.50
977. 978. 979.980.981. 次黄嘌呤
982. 983.984. 136.1115
985. 986.987. 136.11
988. 989.990. 272.22
991. 992. 993.994.995. 肌醇
996. 997.998. 180.16
999. 1000.1001. 180.16
1002. 1003.1004. 360.32
1005. 1006. 1007.1008.1009. NAD +
1010. 1011.1012. 663.43
1013. 1014.1015. 663.43
1016. 1017.1018. 1326.86
1019. 1020. 1021.1022.1023. 对氨基苯甲酸酯
1024. 1025.1026. 137.138
1027. 1028.1029. 137.14
1030. 1031.1032. 274.28
1033. 1034. 1035.1036.1037. 磷酸胆碱
1038. 1039.1040. 184.152
1041. 1042.1043. 329.73
1044. 1045.1046. 659.46
1047. 1048. 1049.1050.1051. 山梨糖醇
1052. 1053.1054. 182.17
1055. 1056.1057. 182.17
1058. 1059.1060. 364.34
1061. 1062. 1063.1064.1065. UDP
1066. 1067.1068. 404.1612
1069. 1070.1071. 448.12
1072. 1073.1074. 896.24
1075. 1076. 1077. 1078.1079. UDP-葡萄糖
1080. 1081.1082. 566.302
1083. 1084.1085. 610.27
1086. 1087.1088. 1220.54
1089. 1090. 1091. 1092. 1093. 注意:将5 mM溶液以37.23 mg / ml溶解于该溶液中。
1094.
1095. 表8.化学标准品库4
1096. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:700px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 1097. <身体> 1098. 1099.1100. 代谢物名称
1101. 1102.1103. 代谢物的分子量
1104. 1105.1106. 化学标准物的分子量
1107. 1108.1109. 称量量(mg)
1110. 1111. 1112.1113.1114. 苯乙酰谷氨酰胺
1115. 1116.1117. 264.3
1118. 1119.1120. 264.3
1121. 1122.1123. 17.17
1124. 1125. 1126.1127.1128. 乙酰谷氨酸
1129. 1130.1131. 189.1659
1132. 1133.1134. 189.1659
1135. 1136.1137. 12.29
1138. 1139. 1140.1141.1142.
1143. 1144.1145.
1146. 1147.1148.
1149. 1150.1151.
1152. 1153. 1154.1155.1156. 乙酰甘氨酸
1157. 1158.1159. 117.1033
1160. 1161.1162. 117.1033
1163. 1164.1165. 7.61
1166. 1167. 1168.1169.1170. 乙酰蛋氨酸
1171. 1172.1173. 191.245
1174. 1175.1176. 191.245
1177. 1178.1179. 12.43
1180. 1181. 1182.1183.1184. 不对称二甲基精氨酸
1185. 1186.1187. 202.25
1188. 1189.1190. 275.2
1191. 1192.1193. 17.88
1194. 1195. 1196.1197.1198. ATP 1199. 1200.1201. 507.18
1202. 1203.1204. 551.14
1205. 1206.1207. 35.82
1208. 1209. 1210.1211.1212. CTP 1213. 1214.1215. 483.1563
1216. 1217.1218. 527.12
1219. 1220.1221. 34.26
1222. 1223. 1224.1225.1226. dATP
1227. 1228.1229. 491.2
1230. 1231.1232. 535.15
1233. 1234.1235. 34.78
1236. 1237. 1238.1239.1240. dCTP
1241. 1242.1243. 467.2
1244. 1245.1246. 511.12
1247. 1248.1249. 33.22
1250. 1251. 1252.1253.1254. 脱氧胞苷
1255. 1256.1257. 227.2172
1258. 1259.1260. 227.2172
1261. 1262.1263. 14.76
1264. 1265. 1266.1267.1268. 叶酸
1269. 1270.1271. 441.3975
1272. 1273.1274. 441.3975
1275. 1276.1277. 28.69
1278. 1279. 1280.1281.1282. GTP
1283. 1284.1285. 523.2
1286. 1287.1288. 523.18
1289. 1290.1291. 34.00
1292. 1293. 1294.1295.1296. 下丘脑
1297. 1298.1299. 109.1475
1300. 1301.1302. 109.1475
1303. 1304.1305. 7.09
1306. 1307. 1308.1309.1310. 蛋氨酸亚砜
1311. 1312.1313. 165.21
1314. 1315.1316. 165.21
1317. 1318.1319. 10.73
1320. 1321. 1322.1323.1324. 甲基硫代腺苷
1325. 1326.1327. 297.3335
1328. 1329.1330. 297.3335
1331. 1332.1333. 19.32
1334. 1335. 1336.1337.1338. 磷酸肌酸
1339. 1340.1341. 211.114
1342. 1343.1344. 255.08
1345. 1346.1347. 16.58
1348. 1349. 1350.1351.1352. 吡rid醇
1353. 1354.1355. 169.18
1356. 1357.1358. 205.64
1359. 1360.1361. 13.36
1362. 1363. 1364.1365.1366. 5-磷酸核糖
1367. 1368.1369. 230.11
1370. 1371.1372. 310.1
1373. 1374.1375. 20.15
1376. 1377. 1378.1379.1380. SAH
1381. 1382.1383. 384.4
1384. 1385.1386. 384.41
1387. 1388.1389. 24.98
1390. 1391. 1392.1393.1394. 胸苷
1395. 1396.1397. 242.2286
1398. 1399.1400. 242.2286
1401. 1402.1403. 15.74
1404. 1405. 1406.1407.1408. 三甲基赖氨酸
1409. 1410.1411. 189.279
1412. 1413.1414. 224.73
1415. 1416.1417. 14.60
1418. 1419. 1420.1421.1422. 尿苷
1423. 1424.1425. 244.2014
1426. 1427.1428. 244.2014
1429. 1430.1431. 15.87
1432. 1433. 1434. 1435.1436. UTP
1437. 1438.1439. 484.1411
1440. 1441.1442. 559.09
1443. 1444.1445. 36.34
1446. 1447. 1448. 1449. 1450. 注意:将溶液以36.75 mg / ml的浓度溶解5 mM溶液。
1451.
1452. 表9. Chemi cal标准库池5
1453. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:900px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 1454. <身体> 1455. 1456.1457. 代谢物名称
1458. 1459.1460. 代谢物的分子量
1461. 1462.1463. 化学标准物的分子量
1464. 1465.1466. 称量量(mg)
1467. 1468. 1469.1470.1471. 3-磷酸甘油酯
1472. 1473.1474. 186.06
1475. 1476.1477. 230.02
1478. 1479.1480. 57.51
1481. 1482. 1483.1484.1485. 顺式乌头酸
1486. 1487.1488. 174.108
1489. 1490.1491. 174.11
1492. 1493.1494. 43.53
1495. 1496. 1497.1498.1499. 柠檬酸盐
1500. 1501.1502. 192.124
1503. 1504.1505. 294.10
1506. 1507.1508. 73.53
1509. 1510. 1511.1512.1513. DHAP
1514. 1515.1516. 170.06
1517. 1518.1519. 180.19
1520. 1521.1522. 45.05
1523. 1524. 1525.1526.1527. 1,6-二磷酸果糖
1528. 1529.1530. 340.1157
1531. 1532.1533. 406.06
1534. 1535.1536. 101.52
1537. 1538. 1539.1540.1541. 富马酸酯
1542. 1543.1544. 116.07
1545. 1546.1547. 116.07
1548. 1549.1550. 29.02
1551. 1552. 1553.1554.1555. 6-磷酸葡萄糖
1556. 1557.1558. 260.135
1559. 1560.1561. 282.12
1562. 1563.1564. 70.53
1565. 1566. 1567.1568.1569. 3-磷酸甘油
1570. 1571.1572. 172.0737
1573. 1574.1575. 370.40
1576. 1577.1578. 92.60
1579. 1580. 1581.1582.1583. 胍基乙酸酯
1584. 1585.1586. 117.1066
1587. 1588.1589. 117.11
1590. 1591.1592. 29.28
1593. 1594. 1595.1596.1597. Kynurenine
1598. 1599.1600. 208.2139
1601. 1602.1603. 208.21
1604. 1605.1606. 52.05
1607. 1608. 1609.1610.1611. 苹果酸
1612. 1613.1614. 134.0874
1615. 1616.1617. 134.09
1618. 1619.1620. 33.52
1621. 1622. 1623.1624.1625. NADP +
1626. 1627.1628. 744.413
1629. 1630.1631. 765.39
1632. 1633.1634. 191.35
1635. 1636. 1637.1638.1639. 烟酰胺
1640. 1641.1642. 122.12
1643. 1644.1645. 122.12
1646. 1647.1648. 30.53
1649. 1650. 1651.1652.1653. 2-草酸戊二酸酯
1654. 1655.1656. 146.11
1657. 1658.1659. 146.11
1660. 1661.1662. 36.53
1663. 1664. 1665.1666.1667. 磷酸烯醇式丙酮酸
1668. 1669.1670. 168.042
1671. 1672.1673. 267.22
1674. 1675.1676. 66.81
1677. 1678. 1679.1680.1681. 丙酮酸
1682. 1683.1684. 88.06
1685. 1686.1687. 110.04
1688. 1689.1690. 27.51
1691. 1692. 1693.1694.1695. 琥珀酸酯
1696. 1697.1698. 118.09
1699. 1700.1701. 118.09
1702. 1703.1704. 29.52
1705. 1706. 1707. 1708.1709. 尿嘧啶
1710. 1711.1712. 112.0868
1713. 1714.1715. 112.09
1716. 1717.1718. 28.02
1719. 1720. 1721. 1722. 1723. 注意:将溶液以20.77 mg / ml的浓度溶解5 mM溶液。
1724.
1725. 表10 化学品标准库6
1726. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:900px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 1727. <身体> 1728. 1729.1730. 代谢物名称
1731. 1732.1733. 代谢物的分子量
1734. 1735.1736. 化学标准物的分子量
1737. 1738.1739. 称量量(mg)
1740. 1741. 1742.1743.1744. 3-羟基异丁酸
1745. 1746.1747. 104.1045
1748. 1749.1750. 126.09
1751. 1752.1753. 22.07
1754. 1755. 1756.1757.1758. 2-羟基戊二酸酯
1759. 1760.1761. 148.114
1762. 1763.1764. 192.10
1765. 1766.1767. 33.62
1768. 1769. 1770.1771.1772. 氨基己二酸酯
1773. 1774.1775. 161.156
1776. 1777.1778. 161.16
1779. 1780.1781. 28.20
1782. 1783. 1784.1785.1786. β-丙氨酸
1787. 1788.1789. 89.093
1790. 1791.1792. 89.09
1793. 1794.1795. 15.59
1796. 1797. 1798.1799.1800. 氨基甲酸酯类
1801. 1802.1803. 176.128
1804. 1805.1806. 176.13
1807. 1808.1809. 30.82
1810. 1811. 1812.1813.1814. 半胱氨酸
1815. 1816.1817. 222.263
1818. 1819.1820. 222.26
1821. 1822.1823. 38.90
1824. 1825. 1826.1827.1828. 半胱氨酸
1829. 1830.1831. 169.16
1832. 1833.1834. 169.16
1835. 1836.1837. 29.60
1838. 1839. 1840.1841.1842. FAD
1843. 1844.1845. 785.5497
1846. 1847.1848. 829.51
1849. 1850.1851. 145.16
1852. 1853. 1854.1855.1856. 甘油磷酸胆碱
1857. 1858.1859. 258.231
1860. 1861.1862. 257.22
1863. 1864.1865. 45.01
1866. 1867. 1868.1869.1870. 肌苷
1871. 1872.1873. 268.229
1874. 1875.1876. 268.23
1877. 1878.1879. 46.94
1880. 1881. 1882.1883.1884. 旋转
1885. 1886.1887. 156.1
1888. 1889.1890. 194.19
1891. 1892.1893. 33.98
1894. 1895. 1896.1897.1898. 泛酸
1899. 1900.1901. 219.23
1902. 1903.1904. 238.27
1905. 1906.1907. 41.70
1908. 1909. 1910.1911.1912. 磷酸丝氨酸
1913. 1914.1915. 185.07
1916. 1917.1918. 185.07
1919. 1920.1921. 32.39
1922. 1923. 1924.1925.1926. 核黄素
1927. 1928.1929. 376.369
1930. 1931.1932. 376.37
1933. 1934.1935. 65.86
1936. 1937. 1938.1939.1940. UDP-GlcNAc
1941. 1942.1943. 607.3537
1944. 1945.1946. 651.32
1947. 1948.1949. 113.98
1950. 1951. 1952. 1953.1954. 尿酸
1955. 1956.1957. 168.1103
1958. 1959.1960. 168.11
1961. 1962.1963. 29.42
1964. 1965. 1966. 1967. 1968. 注意:将溶液以21.52 mg / ml的浓度溶解5 mM溶液。
1969.
1970. 表11.化学标准品库7
1971. class =“ ke-zeroborder” bordercolor =“#000000” style =“ width:900px;” border =“ 0” cellspacing =“ 0” cellpadding =“ 0”> 1972. <身体> 1973. 1974.1975. 代谢物名称
1976. 1977.1978. 分子量的代谢物
1979. 1980.1981. 分子量标准
1982. 1983.1984. 称量量(mg)
1985. 1986. 1987.1988.1989. 衣康酸
1990. 1991.1992. 130.0987
1993. 1994.1995. 130.10
1996. 1997.1998. 52.04
1999. 2000. 2001.2002.2003. 同型半胱氨酸
2004. 2005.2006. 135.185
2007. 2008.2009. 135.19
2010. 2011.2012. 54.07
2013. 2014. 2015.2016.2017. 2-氧代丁酸
2018. 2019.2020. 102.0886
2021. 2022.2023. 102.09
2024. 2025.2026. 40.84
2027. 2028. 2029.2030.2031. 2-羟基丁酸
2032. 2033.2034. 104.1045
2035. 2036.2037. 126.09
2038. 2039.2040. 50.44
2041. 2042. 2043.2044.2045. 抗坏血酸
2046. 2047.2048. 176.1241
2049. 2050.2051. 198.11
2052. 2053.2054. 79.24
2055. 2056. 2057.2058.2059. 肌氨酸
2060. 2061.2062. 89.0932
2063. 2064.2065. 89.09
2066. 2067.2068. 35.64
2069. 2070. 2071.2072.2073. 二甲基甘氨酸
2074. 2075.2076. 103.1198
2077. 2078.2079. 103.12
2080. 2081.2082. 41.25
2083. 2084. 2085.2086.2087. N6-乙酰赖氨酸
2088. 2089.2090. 188.2242
2091. 2092.2093. 188.22
2094. 2095.2096. 75.29
2097. 2098. 2099.2100.2101. Pipecolate
2102. 2103.2104. 129.157
2105. 2106.2107. 129.16
2108. 2109.2110. 51.66
2111. 2112. 2113.2114.2115. 吲哚乳酸酯
2116. 2117.2118. 205.2099
2119. 2120.2121. 205.21
2122. 2123.2124. 82.08
2125. 2126. 2127.2128.2129. 吡啶甲酸
2130. 2131.2132. 123.1094
2133. 2134.2135. 123.11
2136. 2137.2138. 49.24
2139. 2140. 2141.2142.2143. 3-甲基-2-氧代丁酸酯
2144. 2145.2146. 116.1152
2147. 2148.2149. 138.10
2150. 2151.2152. 55.24
2153. 2154. 2155.2156.2157. 3-甲基-2-氧戊酸
2158. 2159.2160. 130.1418
2161. 2162.2163. 152.12
2164. 2165.2166. 60.85
2167. 2168. 2169.2170.2171. 甲酰蛋氨酸
2172. 2173.2174. 177.221
2175. 2176.2177. 177.22
2178. 2179.2180. 70.89
2181. 2182. 2183.2184.2185. 2-氨基丁酸
2186. 2187.2188. 103.1198
2189. 2190.2191. 103.12
2192. 2193.2194. 41.25
2195. 2196. 2197.2198.2199. 同瓜氨酸
2200. 2201.2202. 189.2123
2203. 2204.2205. 189.21
2206. 2207.2208. 75.68
2209. 2210. 2211.2212.2213. γ-谷氨酰丙氨酸
2214. 2215.2216. 217.2224
2217. 2218.2219. 218.21
2220. 2221.2222. 87.28
2223. 2224. 2225.2226.2227. 甘露糖
2228. 2229.2230. 180.16
2231. 2232.2233. 180.16
2234. 2235.2236. 72.06
2237. 2238. 2239. 2240.2241. 半胱氨酸-甘氨酸(二肽)
2242. 2243.2244. 178.21
2245. 2246.2247. 178.21
2248. 2249.2250. 71.28
2251. 2252. 2253. 2254. 2255. 注意:将溶液以14.33 mg / ml的浓度溶解5 mM溶液。
2256.2257.2258.

2259. 致谢 2260.

2261.

2262. 该协议基于我们先前发布的研究(Sullivan等,2019年)。 NIH(F32CA213810)向AM授予了这项工作。 MRS得到T32GM007287的支持,并感谢MIT Koch Institute研究生奖学金的额外支持。 2263.

2264.

2265. 利益争夺 2266.

2267.

2268. 作者报告没有利益冲突。 CAL是ReviveMed的付费顾问。 2269.

2270.

2271. 伦理 2272.

2273.

2274. 这项研究是根据《实验动物的护理和使用指南》中的建议进行的。使用MIT动物护理委员会(IACUC)批准的方案(#1115-110-18)进行所有动物实验。所有手术均使用通过蒸发器进行的异氟醚麻醉进行,并尽一切努力使痛苦最小化。 2275.

2276.

2277. 参考文献 2278.

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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).
引用: 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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