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Oct 2019

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Quantification of Salivary Charged Metabolites Using Capillary Electrophoresis Time-of-flight-mass Spectrometry
唾液中带电荷代谢产物的毛细管电泳与飞行时间质谱联用定量分析   

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Abstract

Salivary metabolomics have provided the potentials to detect both oral and systemic diseases. Capillary electrophoresis time-of-flight-mass spectrometry (CE-TOFMS) enables the identification and quantification of various charged metabolites. This method has been employed to biomarker discoveries using human saliva samples, especially for various types of cancers. The untargeted analysis contributes to finding new biomarkers. i.e., the analysis of all detectable signals including both known and unknown metabolites extends the coverage of metabolite to be observed. However, the observed data includes thousands of peaks. Besides, non-linear migration time fluctuation and skewed peaks are caused by the sample condition. The presented pretreatment protocols of saliva samples enhance the reproducibility of migration time drift, which facilitates the matching peaks across the samples and also results in reproducible absolute concentrations of the detected metabolites. The described protocols are utilized not only for saliva but for any liquid samples with slight modifications.

Keywords: Metabolomics (代谢组学), Capillary electrophoresis-mass spectrometry (毛细管电泳-质谱联用), Saliva (唾液), Polyamine (多胺), Cancer (癌症)

Background

Saliva is one of the biofluids suitable for monitoring the systemic conditions. The non-invasive availability of saliva samples enables frequent, timely, and cost-effective diagnostics, which would contribute to realizing personalized medicine. Therefore biomarker discoveries for various diseases have been reported (Wang et al., 2017). Saliva contains microorganisms and also a wide variety of components, such as genome, coding and non-coding RNA, proteins, and metabolites (Bonne and Wong, 2012; Yoshizawa et al., 2013). Traditionally, the profiling of these components has been utilized for the diagnostic of oral diseases (Dawes and Wong, 2019; Martina et al., 2020). Recently, accumulated evidence has revealed the potential to monitor systematic health conditions (Sugimoto et al., 2013; Kaczor-Urbanowicz et al., 2017).

Among various omics technologies, metabolomics has been utilized to discover the biomarkers for metabolic diseases, including cancers (Trezzi et al., 2015). The metabolic aberrance in cancer cells, such as the Warburg effect (Warburg, 1956), potentially reflects metabolite profiles in biofluids. Metabolomics-based liquid biopsy is therefore intensively developed (Armitage and Ciborowski, 2017). In comparison to the blood and urine-based diagnostics, the use of saliva is an emerging approach while salivary metabolic profiles showed the potential to detect various cancers (Sugimoto et al., 2010a).

The salivary diagnostics of oral cancer are the most reported among various cancers (Washio and Takahashi, 2016; Chattopadhyay and Panda, 2019). We previously confirmed the consistently elevated metabolites in saliva and oral cancer tissues (Ishikawa et al., 2016). The effect of saliva collection after various fasting duration on these markers was also evaluated (Ishikawa et al., 2017). The evaluation of the specificity of oral cancer-specific markers against various diseases in the oral cavity, e.g., periodontal diseases, is also conducted (Mikkonen et al., 2016). Discrimination of oral leukoplakia, oral lichen planus, and oral cancer is practically useful in clinical settings (Sridharan et al., 2019; Ishikawa et al., 2020). The storage condition of the saliva samples affected the discrimination abilities of metabolite markers (Wang et al., 2014), which requires the standard of the protocol to deal with the saliva samples for realizing reproducible diagnostics.

Recently, salivary metabolite biomarkers to diagnose the cancers far from the oral cavity have been reported. The quantified polyamines in saliva were utilized for the detection of breast cancers (Takayama et al., 2016; Murata et al., 2019) using liquid chromatography-mass spectrometry (LC-MS). These analyses were conducted using triple-quadrupole-MS (MS) for targeted analyses. We utilized capillary electrophoresis time-of-flight-MS (CE-TOFMS) to conduct untargeted analyses to quantify hundreds of salivary metabolites for pancreatic cancer detections (Asai et al., 2018). MS requires the ionization of metabolites to be detected and therefore various separation system, detection, and their interface are available (Monton and Soga, 2007). Among these systems, CE-MS is one of a powerful tool that can analyze the charged metabolites using only two modes, cation and anion, i.e., positively and negatively charged metabolites (Soga et al., 2003; Soga, 2007). Here, the metabolite profiling protocol for saliva samples using CE-TOFMS is described.

Materials and Reagents

  1. Pipette tips 100 μl, 10 μl (Eppendorf, catalog numbers: 0030.073.428, 0030 073.363)
  2. 1.5 ml reaction tube (Greiner-Bio-One, catalog number: 616201 )
  3. Nanosep Centrifugal Devices With Omega Membrane 3K, Gray (PALL, catalog number: OD003C34 )
  4. Polypropylene vial (Agilent, catalog number: 5190-3155 )
  5. Snap Cap (Agilent, catalog number: 5042-6491 )
  6. Nitrile gloves (TOP, catalog number: 15731 )
  7. 2 ml Crimp/Snap Top Vials & Caps (Agilent, catalog number: 5182-9697 )
  8. Fused Silica Capillary (Polymicro Technologies, catalog number: TSP050375 )
  9. COSMO(+)Capillary (NACALAI TESQUE, catalog number: 07584-44 )
  10. L-Methionine sulfone, 99+% (Thermo Fisher Scientific Inc, catalog number: A17027 )
  11. 3-Aminopyrrolidine dihydrochloride (Sigma-Aldrich, catalog number: 404624 )
  12. 2-(N-Morpholino)ethanesulfonic Acid (MES) (FUJIFILM Wako Pure Chemical, catalog number: 341-01622 )
  13. D-Camphor-10-sulfonic Acid Sodium Salt (CSA) (FUJIFILM Wako Pure Chemical, catalog number: 037-01032 )
  14. 1,3,5-Benzenetricarboxylic Acid (Trimesate) (FUJIFILM Wako Pure Chemical, catalog number: 206-03641 )
  15. Spermidine (Merk/Sigma-Aldrich, catalog number: S2626 )
  16. Spermine (FUJIFILM Wako Pure Chemical, catalog number: 194-09813 )
  17. N1-Acetylspermine (FUJIFILM Wako Pure Chemical, catalog number: 014-20421 )
  18. N1-Acetylspermidine (Sigma-Aldrich Japan, 01467)
  19. N8-Acetylspermidine (Fluka, catalog number: A3658 )
  20. DL-a-Amino-n-butyric acid (2AB) (Sigma-Aldrich, catalog number: A1754 )
  21. Methanol (FUJIFILM Wako Pure Chemical, catalog number: 134-14523 )
  22. Ammonium Acetate (FUJIFILM Wako Pure Chemical, catalog number: 019-02835 )
  23. Hexakis(2,2-difluoroethoxy)phosphazene (Synquest Laboratories, catalog number: 8H79-3-02 )
  24. Formic Acid Formate (FUJIFILM Wako Pure Chemical, catalog number: 063-05895 )
  25. Ammonium Formate (Kanto Kagaku, catalog number: 01294-00 )
  26. Acetic Acid (FUJIFILM Wako Pure Chemical, catalog number: 012-00245 )
  27. Milli-Q water (MERCK, Tokyo, Japan, Milli-Q IQ 7000 ICP-MS)
  28. Internal standards mixture (see Recipes)
  29. Sheath liquid (see Recipes)
  30. Run buffer (see Recipes)
  31. Standard mixture (see Recipes)

Equipment

  1. Measuring flask 5 ml (IWAKI, catalog number: 5640FK5S )
  2. Laboratory Bottles 100 ml (SCHOTT/DURAN, catalog number: 017200-100A )
  3. Pipettes 100 μl, and 10 μl (Eppendorf, catalog numbers: 3121 000.074, 3121 000.015)
  4. Centrifuge (TOMY, model: MX-307 )
  5. Roter (TOMY, model: AR015-24 )
  6. MicroMixer (TAITEC, model: E-36 )
  7. CE system (Agilent Technologies, model: 7100 )
  8. LC/MSD TOF system (Agilent, catalog number: G1969A )
  9. Isocratic HPLC pump (Agilent, model: 1260 series )
  10. CEMS adapter kit (Agilent, catalog number: G1603A )
  11. CE-ESI-MS sprayer kit (Agilent, catalog number: G1607A )
  12. Circulation type hand cooler (THOMAS, catalog number: 2241059 )

Software

  1. Agilent Chemstation software was used for CE, ver. B.02.01.SR1 (Agilent Technologies, Santa Clara, CA, USA)
  2. Agilent MassHunter software, ver. B.02.01.SR1 (Agilent) for TOF-MS data analyses
  3. R, ver. 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria)
  4. JMP, ver. 13.2.0 (SAS Institute Inc., Cary, NC, USA)
  5. WEKA, ver. 3.6.13 (The University of Waikato, Hamilton, New Sealand)
  6. GraphPad Prism, ver 7.03 (GraphPad Software Inc., San Diego, CA, USA)
  7. MasterHands, ver 2.17.3.18 (Keio University, Tsuruoka, Japan)

Procedure

Prepare all solutions and mixtures at room temperature. All sample processing is conducted using Nitrile gloves. Anaerobic condition is not necessary.

  1. Saliva collection
    1. Subjects with a primary disease without a history of prior malignancy.
    2. Subjects who did not receive any prior treatment in the form of chemotherapy, radiotherapy, surgery, or alternative therapy.
    3. The saliva providers were not allowed to take any food except water intake after 9:00 p.m. on the previous day.
    4. All samples were collected from 8:00 to 11:00 a.m.
    5. The subjects were required to brush their teeth without toothpaste on the day of saliva collection and had to refrain from drinking water, smoking, tooth-brushing, and intense exercise 1 h before saliva collection.
    6. They were required to gargle with water just before saliva collection.
    7. Approximately 400 μl of unstimulated saliva was collected in a 50 cc polypropylene tube (see Note 1).
    8. A polypropylene straw 1.1 cm in diameter was used to assist the saliva collection.
    9. After collection, saliva samples were immediately stored at -80 °C.

  2. Processing of saliva samples.
    The overview of saliva processing is depicted in Figure 1.


    Figure 1. A protocol of saliva processing

    1. A frozen saliva sample is thawed at 4 °C.
    2. 100 μl of the saliva sample is moved to a 1.5-ml tube with a 5-kDa cutoff filter using a pipet.
    3. The tube is centrifuged at 9,100 × g for 3 h at 4 °C to eliminate large molecules.
    4. If the filtrate is not less than 45 μl (Figure 2), the rest of the raw saliva samples should be processed independently (Steps B2 and B3) and the filtrate should be merged.


      Figure 2. A tube with a filter. The filtrate of 45 μl is shown.

    5. 45 μl of the filtrate is moved to a 1.5 ml of microtube using a pipet.
    6. 5 μl of water containing an internal standards mixture (Recipe 1) was added to the tube using a pipet, mixed 30 s using vortex, and centrifuged at 4,550 × g for 1 min at 4 °C to mix well.
    7. 7 μl of the mixture is transferred to a vial and the vial is covered with a snap cap.

  3. Setup for the cation measurement
    1. CE
      1. Capillary : Fused-silica, i.d. 50 μm × 100 cm
      2. Buffer : 1 M Formic acid
      3. Voltage : Positive, 30 kV
      4. Temperature : 20 °C
      5. Injection : Pressure injection 50 mbar, 5 s (approximately 5 nl)
    2. TOFMS
      1. Ion Source : Electrospray ionization (ESI)
      2. Polarity : Positive
      3. Capillary voltage : 4,000 V
      4. Fragmentor : 75 V
      5. Skimmer : 50 V
      6. OCT RFV : 500 V
      7. Drying gas : N2, 10 L/min
      8. Drying gas temp. : 300 °C
      9. Nebulizer gas press. : 7 psig
      10. Sheath liquid: Cation sheath liquid (Recipe 2)
      11. Flow rate : 10 μl/min
      12. Lock mass : 2MeOH13Cisotope m/z 66.063061 and Hexakis(2,2-difluoroethoxy)phosphazene m/z 622.028963

  4. Setup for the anion measurement
    1. CE
      1. Capillary : COSMO(+), i.d. 50 μm × 105 cm
      2. Buffer : 50 mM Ammonium acetate, pH 8.5
      3. Voltage : Negative, 30 kV
      4. Temperature : 20 °C
      5. Injection : Pressure injection 50 mbar, 30 s (approximately 30 nl)
      6. Preconditioning : 2 min at 50 mM Ammonium acetate, pH 3.4 and 5 min at run buffer
    2. TOFMS
      1. Ion Source : ESI
      2. Polarity : Negative
      3. Capillary voltage : 3,500 V
      4. Fragmentor : 100 V
      5. Skimmer : 50 V
      6. OCT RFV : 200 V
      7. Drying gas : N2, 10 L/min
      8. Drying gas temp. : 300 °C
      9. Nebulizer gas press. : 7 psig
      10. Sheath liquid: Anion sheath liquid (see Recipes)
      11. Flow rate : 10 μl/min
      12. Lock mass : 2CH3COOH13C isotope m/z 120.038339, Hexakis(2,2-difluoroethoxy)phosphazene+CH3COOH m/z 680.035541
      13. ESI needle : Platinum
    3. Measurement
      1. Sample preconditioning
        1. Cation for standard mixtures: 4 min at a cation run buffer (see Recipes).
        2. Cation for samples: 5 min at Ammonium Formate, 5 min at Milli-Q, and 5 min at an anion run buffer (Recipe 3).
        3. Anion for standard mixtures and samples: 2 min at 50 mM Ammonium acetate, pH 3.4, and 5 min at a cation run buffer (Recipe 3).
      2. Three samples and a standard mixture are measured for accessing the quality.
      3. Evaluate the stability of the total ion electrophoresis (TIE) (Figures 3 and 4). If the TIE is unstable, adjust the ESI sprayer until the intensities of the TIE becomes steady.


        Figure 3. An example of the stable TIE of the cation measurement. The X-axis indicates the migration time. The Y-axis indicates the intensities of TIE, the sum of intensities over the m/z range in the observed data. Under the successful conditioning, this line becomes flat except for a few parts. Two significant drops of the TIE are observed at the left because of the existence of the inorganic cations, such as Na+ and K+. Two large peaks are observed of the existence of neutral molecules. The other small alternations are negligible and the flat of the other part indicates the stability of the conditioning.


        Figure 4. An example of the unstable TIE of the cation measurement. Compared to the TIE line in Figure 3, the line here fluctuates, which indicate the unsuccessful conditioning. Most of the cases, the adjustment of ESI sprayer is not suitable and the electric current fluctuates, which causes such TIE fluctuations.

      4. Evaluate the stability of electric current during the measurement (Figures 5 and 6). Dilute the final solution and measure the sample again if the TIE is unstable.


        Figure 5. An example of the stable electric current of the cation measurement. The X-axis indicates the migration time. The Y-axis indicates the electric current during the measurement. Under the successful measurement, this line becomes flat except for the start of the measurement.


        Figure 6. An example of the unstable electric current of the cation measurement. The X-axis indicates the migration time. The Y-axis indicates the electric current during the measurement. This example shows many spike peaks. When the condition of capillary becomes abnormal, the line doesn't stay flat.

      5. Measure saliva samples.

Data analysis

Data analysis includes data processing and statistical analysis (Figure 7). The data processing includes data conversion and peak integration for each raw data. The process data were aligned and a data matrix is yielded after filling missing peaks (Sugimoto et al., 2012). This matrix is used for the subsequent data analysis, i.e., statistical analysis.


Figure 7. Overview of data processing and data analysis

  1. For each sample, a data folder (.d extension) is yielded by MassHunter, a vendor-supplied software for CE-MS controls. Data folders of the standard mixture, quality control, and saliva samples are prepared.
  2. Each data folder is converted to another format file (.kiff extension) using a data conversion function of MasterHands (Sugimoto et al., 2012)
  3. Subsequent data processing will be conducted using MasterHands
  4. Detect and integrate peaks with default options of all data files; signal/noise (S/N) radio > 2.0 for peak detection and 50% of the depth between two peaks for peak separation (see Note 2).
  5. Run alignment function to correct and matching of the peaks across all samples is conducted with default options. If the alignment function is not stable (Figure 8), rerun the alignment with the different gap penalty option (Sugimoto et al., 2010b).


    Figure 8. Examples of alignment functions. Stable (left) and unstable (right) cases.

  6. Evaluate the overlaid peaks for internal standards. If the overlaid peaks are not observed, rerun the alignment with the different options (Figure 9).


    Figure 9. Examples of overlaid peaks. Successful (left) and unsuccessful (right) results.

  7. Run reintegration function to filling missing peaks based on the aligned peak matrix.
  8. Exchange the peak area less than the lower limit of quantification to 0.
  9. The peak area of all peaks will be generated and these peak areas are divided by that of internal standard (methionine sulfone for cation and CSA for anion).
  10. Based on the corrected migration times and m/z values among standard mixture and samples, the metabolite names will be assigned.
  11. Export the peak matrix as a CSV file.
  12. Calculate the absolute concentration of the salivary metabolite concentrations.


    where, M, IS, STD, PA, V indicates metabolite, internal standard, standard, peak area, and volume, respectively.

Statistical analysis
  1. Conduct principal component analysis (PCA) using JMP to visualize the overall similarity and discrepancy of the metabolite concentration patterns among salivary samples (see Note 3).
  2. Conduct the Mann–Whitney test using R to access the difference of metabolite concentrations between 2 groups. The R function of wilcox.test() with correct = F options is used (see Note 4).
  3. Correct P-values using R using false discovery rate (Benjamini and Hochberg methods), considering multiple independent tests. The adjust() method of R vector with method = “fdr” options is used.
  4. Select only metabolite showing corrected P < 0.05 and fold change (F.C.) > 4.0 of the averaged concentrations between pancreatic patients (PC) and non-PC groups (healthy controls with chronic pancreatitis).
  5. Select the minimum metabolite set for MLR using JMP. Select the option of mixed selection (the combination of backward and forward selections with P < 0.05) to eliminate multicollinearity. Select [analysis]-[fit model menu] on JMP, and set stepwise options for personality. Select the “P-value Threshold” for stopping rule and select “mixed” for the direction. Input 0.05 for the threshold and press the “Go” button.
  6. Develop a multiple logistic regression (MLR) model to discriminate patients from non-PC and calculate an area under the receiver operating characteristic curves (AUC). Press the “Run model” button.
  7. Evaluate the generalization ability of the MLR model using Weka. Run k-fold cross-validations (CVs) to evaluate the generalization ability of the developed MLR. Repeat a CV test with 200 random values and k = 5, 10, and 20 (totally, 600 tests). Run weka.classifiers.functions.Logistic class of weka package with –x k (k is replaced to numerical values).
  8. Correct AUC values to calculate its mean and 95% confidential intervals. Select [Analyze Data]-[ROC Curve] menu using GraphPad.

Notes

  1. The use of polypropylene should be evaluated beforehand. Using the presented protocol, we confirmed that no effect of eluted compounds on the quantification quality of the saliva metabolites. However, this confirmation is limited to our standard library. Various condition of storage and solvent was compared (Tomita et al., 2018). Based on the observed fluctuation of these data, we computationally produced noise on the quantification level of the salivary metabolite and evaluated the effect of salivary biomarkers (Asai et al., 2018) on their discrimination abilities. Based on these data, standardization of operating procedures should be defined.
  2. Calibration ranges for each metabolite should be defined using a standard mixture before the analysis of saliva samples. In our protocol, we confirmed that the metabolites in our standard library showed high linearity between the peak area and the concentration up to 1 mM. Based on these data, the upper quantification range was defined 500 M, and the corresponding peak area was calculated beforehand. The peak areas showing larger than this threshold are considered as saturated peaks. The peaks showing S/N < 1.5 are considered as lower quantification limit.
  3. For PCA analyses, only frequently observed metabolite data should be used. We used metabolites detected at least 50% per group (Asai et al., 2018), while this threshold depends on the number of samples and the number of metabolites.
  4. The comparison of multiple groups depends on the study design. The salivary biomarker discovery study to detect pancreatic cancer included control (C), pancreatic cancer (PC), and chronic pancreatitis (CP) (Asai et al., 2018). Usually, a Kruskal-Wallis test should be used for the comparison of three groups. Here, we aimed to find metabolite showing high concentration only in a PC group. Therefore, we treated C and CP as one group and used a Mann-Whitney test to compare PC vs (C + CP) groups.

Recipes

  1. Internal standards mixture
    2 mM of Methionine sulfone
    2 mM of MES
    2 mM of CSA
    2 mM of 3-Aminopyrrolidine
    2 mM of Trimesate
  2. Sheath liquid
    Cation: Methanol/water (50% v/v) containing 0.1 μM Hexakis (2,2-difluorothoxy) phosphazene
    Anion: Ammonium acetate (5 mmol/L) in 50% methanol/water (50% v/v) containing 0.1 μM Hexakis (2,2-difluorothoxy) phosphazene
  3. Run buffer
    Cation: 1 M of Formic acid
    Anion: 50 mM Ammonium acetate (pH 3.4 and pH 8.5)
  4. Standard mixture
    Cation: 200 μM of internal standards and 20 μM of each metabolite
    Anion: 200 μM of internal standards and 50 μM of each metabolite

Acknowledgments

This work was supported by a grant from the Program on Open Innovation Platform with Enterprises, Research Institute and Academia (OPERA, JPMJOP1842), and grants from Tsuruoka City and Yamagata Prefecture.

Competing interests

There are no competing interests to be declared.

Ethics

This study was approved by the ethics committee of Tokyo Medical University (approval no. 1560, 30 September 2010). Written informed consent was obtained from all patients and from volunteers who agreed to serve as saliva donors. Our study was carried out following the Helsinki Declaration.

References

  1. Armitage, E. G. and Ciborowski, M. (2017). Applications of metabolomics in cancer studies. Adv Exp Med Biol 965: 209-234. 
  2. Asai, Y., Itoi, T., Sugimoto, M., Sofuni, A., Tsuchiya, T., Tanaka, R., Tonozuka, R., Honjo, M., Mukai, S., Fujita, M., Yamamoto, K., Matsunami, Y., Kurosawa, T., Nagakawa, Y., Kaneko, M., Ota, S., Kawachi, S., Shimazu, M., Soga, T., Tomita, M. and Sunamura, M. (2018). Elevated polyamines in saliva of pancreatic cancer. Cancers (Basel) 10(2). 
  3. Bonne, N. J. and Wong, D. T. (2012). Salivary biomarker development using genomic, proteomic and metabolomic approaches. Genome Med 4(10): 82.
  4. Chattopadhyay, I. and Panda, M. (2019). Recent trends of saliva omics biomarkers for the diagnosis and treatment of oral cancer. J Oral Biosci 61(2): 84-94.
  5. Dawes, C. and Wong, D. T. W. (2019). Role of saliva and salivary diagnostics in the advancement of oral health. J Dent Res 98(2): 133-141. 
  6. Ishikawa, S., Sugimoto, M., Edamatsu, K., Sugano, A., Kitabatake, K. and Iino, M. (2020). Discrimination of oral squamous cell carcinoma from oral lichen planus by salivary metabolomics. Oral Dis 26(1): 35-42. 
  7. Ishikawa, S., Sugimoto, M., Kitabatake, K., Sugano, A., Nakamura, M., Kaneko, M., Ota, S., Hiwatari, K., Enomoto, A., Soga, T., Tomita, M. and Iino, M. (2016). Identification of salivary metabolomic biomarkers for oral cancer screening. Sci Rep 6: 31520.
  8. Ishikawa, S., Sugimoto, M., Kitabatake, K., Tu, M., Sugano, A., Yamamori, I., Iba, A., Yusa, K., Kaneko, M., Ota, S., Hiwatari, K., Enomoto, A., Masaru, T. and Iino, M. (2017). Effect of timing of collection of salivary metabolomic biomarkers on oral cancer detection. Amino Acids 49(4): 761-770.
  9. Kaczor-Urbanowicz, K. E., Martin Carreras-Presas, C., Aro, K., Tu, M., Garcia-Godoy, F. and Wong, D. T. (2017). Saliva diagnostics - Current views and directions. Exp Biol Med (Maywood) 242(5): 459-472.
  10. Martina, E., Campanati, A., Diotallevi, F. and Offidani, A. (2020). Saliva and oral diseases. J Clin Med 9(2). 
  11. Mikkonen, J. J., Singh, S. P., Herrala, M., Lappalainen, R., Myllymaa, S. and Kullaa, A. M. (2016). Salivary metabolomics in the diagnosis of oral cancer and periodontal diseases. J Periodontal Res 51(4): 431-437. 
  12. Monton, M., R., and Soga, T. (2007). Metabolome analysis by capillary electrophoresis-mass spectrometry. J Chromatogr A 168(1-2): 237-246.
  13. Murata, T., Yanagisawa, T., Kurihara, T., Kaneko, M., Ota, S., Enomoto, A., Tomita, M., Sugimoto, M., Sunamura, M., Hayashida, T., Kitagawa, Y. and Jinno, H. (2019). Salivary metabolomics with alternative decision tree-based machine learning methods for breast cancer discrimination. Breast Cancer Res Treat 177(3): 591-601. 
  14. Soga, T., Ohashi, Y., Ueno, Y., Naraoka, H., Tomita, M., and Nishioka, T. (2003). Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J Proteome Res 2(5): 488-494.
  15. Soga, T. (2007). Capillary electrophoresis-mass spectrometry for metabolomics. Methods Mol Biol 358:129-137.
  16. Sridharan, G., Ramani, P., Patankar, S. and Vijayaraghavan, R. (2019). Evaluation of salivary metabolomics in oral leukoplakia and oral squamous cell carcinoma. J Oral Pathol Med 48(4): 299-306.
  17. Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T. and Tomita, M. (2010a). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 6(1): 78-95.
  18. Sugimoto, M., Kawakami, M., Robert, M., Soga, T., Tomita, and M. (2012). Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Curr Bioinform 7(1): 96-108.
  19. Sugimoto, M., Saruta, J., Matsuki, C., To, M., Onuma, H., Kaneko, M., Soga, T., Tomita, M. and Tsukinoki, K. (2013). Physiological and environmental parameters associated with mass spectrometry-based salivary metabolomic profiles. Metabolomics 9(2): 454-463. 
  20. Sugimoto, M., Hirayama, A., Ishikawa, T., Robert, M., Baran, R., Uehara, K., Kawai, K., Soga, T. and Tomita, M. (2010b). Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis. Metabolomics 6(1): 27-41. 
  21. Takayama, T., Tsutsui, H., Shimizu, I., Toyama, T., Yoshimoto, N., Endo, Y., Inoue, K., Todoroki, K., Min, J. Z., Mizuno, H. and Toyo'oka, T. (2016). Diagnostic approach to breast cancer patients based on target metabolomics in saliva by liquid chromatography with tandem mass spectrometry. Clin Chim Acta 452: 18-26. 
  22. Tomita, A., Mori, M., Hiwatari, K., Yamaguchi, E., Itoi, T., Sunamura, M., Soga, T., Tomita, M,, Sugimoto, M. (2018). Effect of storage conditions on salivary polyamines quantified via liquid chromatography-mass spectrometry. Sci Rep 8(1):12075.
  23. Trezzi, J. P., Vlassis, N. and Hiller, K. (2015). The role of metabolomics in the study of cancer biomarkers and in the development of diagnostic tools. Adv Exp Med Biol 867: 41-57.
  24. Wang, Q., Gao, P., Wang, X. and Duan, Y. (2014). Investigation and identification of potential biomarkers in human saliva for the early diagnosis of oral squamous cell carcinoma. Clin Chim Acta 427: 79-85. 
  25. Wang, X., Kaczor-Urbanowicz, K. E. and Wong, D. T. (2017). Salivary biomarkers in cancer detection. Med Oncol 34(1): 7. 
  26. Warburg, O. (1956). On respiratory impairment in cancer cells. Science 124(3215): 269-270.
  27. Washio, J. and Takahashi, N. (2016). Metabolomic studies of oral biofilm, oral cancer, and beyond. Int J Mol Sci 17(6). 
  28. Yoshizawa, J. M., Schafer, C. A., Schafer, J. J., Farrell, J. J., Paster, B. J. and Wong, D. T. (2013). Salivary biomarkers: toward future clinical and diagnostic utilities. Clin Microbiol Rev 26(4): 781-791.

简介

[摘要] 唾液代谢组学为口腔和全身疾病的检测提供了可能。毛细管电泳-飞行时间质谱(CE-TOFMS)能够识别和定量各种带电荷的代谢物。这种方法已被用于利用人类唾液样本发现生物标志物,特别是对各种癌症。非靶向分析有助于寻找新的生物标志物。i、 对所有可检测信号的分析,包括已知和未知的代谢物,扩大了待观察代谢物的覆盖范围。然而,观测数据包括数千个峰值。此外,样品条件也会导致偏移时间的非线性波动和峰值的倾斜。本文提出的唾液样品预处理方案提高了迁移时间漂移的再现性,有助于样品间的匹配峰,并使检测到的代谢物的绝对浓度可重复。所述方案不仅适用于唾液,而且适用于任何稍加修改的液体样品。

[背景] 唾液是一种适合监测全身状况的生物流体。唾液样本的无创性可获得性,可进行频繁、及时、经济的诊断,这将有助于实现个性化医疗。因此,各种疾病的生物标志物发现已被报道(Wang等人,2017年)。唾液中含有微生物和多种成分,如基因组、编码和非编码RNA、蛋白质和代谢物(Bonne和Wong,2012;Yoshizawa等人,2013)。传统上,这些成分的分析用于口腔疾病的诊断(Dawes和Wong,2019;Martina等人,2020)。最近,积累的证据显示了监测系统健康状况的潜力(Sugimoto等人,2013年;Kaczor-Urbanowicz等人,2017年)。
在各种组学技术中,代谢组学已被用于发现代谢性疾病的生物标志物,包括癌症(Trezzi等人,2015年)。癌细胞中的代谢异常,如沃堡效应(Warburg,1956),可能反映了生物流体中的代谢物分布。因此,基于代谢组学的液体活检技术得到了广泛的发展(Armitage和Ciborowski,2017)。与基于血液和尿液的诊断相比,使用唾液是一种新兴的方法,而唾液代谢谱显示了检测各种癌症的潜力(Sugimoto等人,2010a)。
口腔癌的唾液诊断是各种癌症中报告最多的(Washio和Takahashi,2016;Chattopadhayy和Panda,2019)。我们先前证实唾液和口腔癌组织中的代谢物持续升高(Ishikawa等人,2016年)。还评估了不同禁食时间后收集唾液对这些标记物的影响(Ishikawa等人,2017年)。还对口腔癌特异性标记物对口腔内各种疾病(如牙周病)的特异性进行了评估(Mikkonen,2016)。口腔白斑、口腔扁平苔藓和口腔癌的鉴别在临床上非常有用(Sridharan,2019年;Ishikawa,2020年)。唾液样本的储存条件影响了代谢物标记物的鉴别能力(Wang,2014),这就要求协议标准对唾液样本进行处理,以实现可重复诊断。等等。等等。等等。等等。
近年来,唾液代谢物生物标志物已被报道用于诊断远离口腔的癌症。唾液中量化的多胺用于使用液相色谱-质谱(LC-MS)检测乳腺癌(Takayama,2016;Murata,2019)。这些分析是使用三重四极杆质谱(MS)进行针对性分析。我们利用毛细管电泳飞行时间质谱(CE-TOFMS)进行非靶向分析,量化用于胰腺癌检测的数百种唾液代谢物(Asai,2018)。MS需要对代谢物进行离子化检测,因此可以使用各种分离系统、检测及其接口(Monton和Soga,2007)。在这些系统中,CE-MS是一种强大的工具,它可以仅使用阳离子和阴离子两种模式来分析带电荷的代谢物,即带正电和带负电的代谢物(Soga等人,2003;Soga,2007)。这里,描述了使用CE-TOFMS对唾液样本进行代谢物分析的方法。等等。等等。等等。

关键字:代谢组学, 毛细管电泳-质谱联用, 唾液, 多胺, 癌症

材料和试剂
 
1.     100μl,10μl移液管头(Eppendorf,目录号:0030.073.428,0030 073.363)
2.     1.5 ml反应管(Greiner Bio One,产品编号:616201)
3.     Nanosep离心装置,带欧米茄膜3K,灰色(PALL,目录号:OD003C34)
4.     聚丙烯瓶(安捷伦,目录号:5190-3155)
5.     弹簧帽(安捷伦,目录号:5042-6491)
6.     丁腈手套(顶部,目录号:15731)
7.     2ml压接/扣盖小瓶和瓶盖(安捷伦,目录号:5182-9697)
8.     熔融石英毛细管(Polymicro Technologies,目录号:TSP050375)
9.     COSMO(+)毛细管(NACALAI TESQUE,目录号:07584-44)
10.  L-蛋氨酸砜,99+%(赛默飞世尔科学公司,目录号:A17027)
11.  3-氨基吡咯烷盐酸盐(Sigma-Aldrich,目录号:404624)
12.  2-(N-吗啉基)乙磺酸(MES)(FUJIFILM Wako Pure Chemical,目录号:341-01622)
13.  D-樟脑-10-磺酸钠盐(CSA)(FUJIFILM Wako Pure Chemical,目录号:037-01032)
14.  1,3,5-苯三甲酸(Trimesate)(FUJIFILM Wako Pure Chemical,目录号:206-03641)
15.  亚精胺(Merk/Sigma-Aldrich,目录号:S2626)
16.  精胺(FUJIFILM Wako Pure Chemical,目录号:194-09813)
17.  N1乙酰精胺(FUJIFILM Wako Pure Chemical,目录号:014-20421)
18.  N1乙酰亚精胺(Sigma-Aldrich日本,01467)
19.  N8乙酰亚精胺(Fluka,目录号:A3658)
20.  DL-a-氨基-n-丁酸(2AB)(西格玛·奥尔德里奇,目录号:A1754)
21.  甲醇(FUJIFILM Wako Pure Chemical,目录号:134-14523)
22.  醋酸铵(FUJIFILM Wako Pure Chemical,目录号:019-02835)
23.  六(2,2-二氟乙氧基)磷腈(Synquest Laboratories,目录号:8H79-3-02)
24.  甲酸甲酸(FUJIFILM Wako Pure Chemical,目录号:063-05895)
25.  甲酸铵(Kanto Kagaku,目录号:01294-00)
26.  乙酸(FUJIFILM Wako Pure Chemical,目录号:012-00245)
27.  Milli-Q水(默克,日本东京,Milli-Q IQ 7000 ICP-MS)
28.  内标混合物(见配方)
29.  鞘液(见配方)
30.  运行缓冲区(见配方)
31.  标准混合物(见配方)
 
设备
 
1.     5 ml量瓶(IWAKI,目录号:5640FK5S)
2.     实验室瓶100毫升(SCHOTT/DURAN,目录号:017200-100A)
3.     100μl和10μl移液管(Eppendorf,目录号:3121 000.074、3121 000.015)
4.     离心机(TOMY,型号:MX-307)
5.     Roter(TOMY,型号:AR015-24)
6.     微混合器(TAITEC,型号:E-36)
7.     CE系统(安捷伦科技,型号:7100)
8.     LC/MSD TOF系统(安捷伦,目录号:G1969A)
9.     等比例高效液相色谱泵(安捷伦,型号:1260系列)
10.  CEMS适配器套件(安捷伦,目录号:G1603A)
11.  CE-ESI-MS喷雾器套件(安捷伦,目录号:G1607A)
12.  循环式手冷器(THOMAS,目录号:2241059)
 
软件
 
1.     安捷伦化学工作站软件用于CE,ver。B、 02.01.SR1(安捷伦科技公司,加利福尼亚州圣克拉拉市,美国)
2.     安捷伦Mashunter软件,版本。B、 02.01.SR1(安捷伦),用于TOF-MS数据分析
3.     R、 版本。3.4.3(R统计计算基金会,奥地利维也纳)
4.     吉咪,弗。13.2.0(SAS Institute Inc.,美国北卡罗来纳州卡里)
5.     韦卡,弗。3.6.13(怀卡托大学,汉密尔顿,新西兰)
6.     GraphPad Prism,7.03版(GraphPad软件公司,加利福尼亚州圣地亚哥,美国)
7.     MasterHands,2.17.3.18版(日本筑冈庆应大学)
 
程序
 
在室温下制备所有溶液和混合物。所有样品处理均使用丁腈手套。不需要厌氧条件。
 
A、 唾液收集
1.     既往无恶性肿瘤病史的原发性疾病患者。
2.     先前未接受任何化疗、放疗、手术或替代治疗的受试者。
3.     在前一天晚上9:00以后,唾液提供者除了喝水外,不允许进食任何食物。
4.     所有样本采集时间为上午8:00至11:00。
5.     要求受试者在唾液收集当天不使用牙膏刷牙,并且在收集唾液前1小时不得喝水、吸烟、刷牙和剧烈运动。
6.     他们被要求在收集唾液之前用水漱口。
7.     在50cc聚丙烯管中收集约400μl未刺激唾液(见注1)。
8.     用直径1.1cm的聚丙烯吸管辅助唾液收集。
9.     采集后,唾液样本立即保存在-80。摄氏度
 
B、 唾液样本的处理。
唾液处理的概述如图1所示。
 
 
图1。唾液处理方案
1.     冷冻唾液样本在4℃下解冻。
2.     用移液管将100μl唾液样品移到1.5-ml的管中,该管带有5-kDa截止过滤器。
3.     试管在9100×g下在4°C下离心3小时,以消除大分子。
4.     如果滤液不少于45μl(图2),其余的原始唾液样本应单独处理(步骤B2和B3),并合并滤液。
 
 
图2。带过滤器的管子。滤液为45μl。
 
5.     用移液管将45μl滤液移到1.5 ml的微管中。
6.     使用移液管将含有内标混合物(配方1)的5μl水添加到试管中,使用涡流混合30 s,并在4550×g下在4°C下离心1分钟,以充分混合。
7.     将7μl混合物转移到一个小瓶中,并在小瓶上盖上一个扣盖。
 
C、 阳离子测量设置
1.     总工程师
a、 毛细管:熔融石英,内径50μm×100 cm
b、 缓冲液:1M甲酸
c、 电压:正,30 kV
d、 温度:20°C
e、 喷射:压力喷射50 mbar,5 s(约5 nl)
2.     TOFMS公司
a、 离子源:电喷雾电离(ESI)
b、 极性:正极
c、 毛细管电压:4000V
d、 破碎器:75 V
e、 撇渣器:50 V
f、 OCT射频:500 V
g、 干燥气体:氮气,10 L/min
h、 干燥气体温度。:300摄氏度
i、 喷雾器气压。:7磅/平方英寸
j、 鞘液:阳离子鞘液(配方2)
k、 流速:10μl/min
l、 锁固质量:2MeoH13同位素m/z 66.063061和六(2,2-二氟乙氧基)磷腈m/z 622.028963
 
D、 负离子测量设置
1.      总工程师
a、 毛细管:COSMO(+),内径50μm×105 cm
b、 缓冲液:50 mM醋酸铵,pH 8.5
c、 电压:负极,30 kV
d、 温度:20°C
e、 喷射:压力喷射50 mbar,30 s(约30 nl)
f、 预处理:在50 mM醋酸铵下2 min,pH 3.4,在运行缓冲液中5 min
2.     TOFMS公司
a、 离子源:ESI
b、 极性:负极
c、 毛细管电压:3500 V
d、 破碎机:100 V
e、 撇渣器:50 V
f、 OCT射频:200伏
g、 干燥气体:氮气,10 L/min
h、 干燥气体温度。:300摄氏度
i、 喷雾器气压。:7磅/平方英寸
j、 鞘液:阴离子鞘液(见配方)
k、 流速:10μl/min
l、 锁固质量:2CH3COOH13C同位素m/z 120.038339,六(2,2-二氟乙氧基)磷腈+CH3COOH m/z 680.035541
m、 ESI针:铂金
3.     测量
a、 样品预处理
i、 标准混合物的阳离子:在阳离子缓冲液中4分钟(见配方)。
二。样品的阳离子:甲酸铵溶液中5分钟,Milli-Q溶液中5分钟,阴离子流动缓冲液中5分钟(配方3)。
iii.标准混合物和样品的阴离子:在50 mM乙酸铵(pH 3.4)下2 min,在阳离子流动缓冲液(配方3)下5 min。
b、 测量三个样品和一个标准混合物以获得质量。
c、 评估总离子电泳(TIE)的稳定性(图3和图4)。如果扎带不稳定,调整ESI喷洒器,直到扎带强度稳定。
 
 
图3。阳离子测量稳定关系的一个例子。 X轴表示迁移时间。Y轴表示TIE的强度,即观测数据中m/z范围内强度的总和。在成功的条件下,这条线变平了,除了一些部分。由于无机阳离子如Na+和K+的存在,在左侧观察到两个显著的TIE滴。观察到中性分子存在的两个大峰。其他小的变化可以忽略不计,而另一部分的平坦表明了调节的稳定性。
 
 
图4。阳离子测量不稳定的一个例子。 与图3中的连接线相比,这里的线是波动的,这表明调节不成功。在大多数情况下,ESI喷雾器的调节不合适,电流波动较大,从而引起这种波动。
 
d、 在测量过程中评估电流的稳定性(图5和图6)。稀释最终溶液,如果领带不稳定,再次测量样品。
 
 
图5。电流稳定测量的一个例子。 X轴表示迁移时间。Y轴表示测量过程中的电流。在成功测量的情况下,除开始测量外,该线变平。
 
 
图6。阳离子测量中不稳定电流的一个例子。 X轴表示迁移时间。Y轴表示测量过程中的电流。这个例子显示了许多尖峰。当毛细血管的状况变得异常时,线就不会保持平坦。
 
e、 测量唾液样本。
 
数据分析
 
数据分析包括数据处理和统计分析(图7)。数据处理包括每个原始数据的数据转换和峰值积分。对工艺数据进行校准,并在填充缺失峰后生成数据矩阵(Sugimoto等人,2012年)。该矩阵用于后续数据分析,即统计分析。
 
 
图7。数据处理和数据分析概述
 
1.     对于每个示例,MassHunter都会生成一个数据文件夹(.d扩展名),MassHunter是一个供应商为CE-MS控件提供的软件。准备标准混合物、质量控制和唾液样本的数据文件夹。
2.     使用MasterHands的数据转换功能将每个数据文件夹转换为另一个格式文件(.kiff扩展名)(Sugimoto等人,2012)
3.     随后的数据处理将使用MasterHands进行
4.     使用所有数据文件的默认选项检测并整合峰值;对于峰值检测,信号/噪声(S/N)无线电>2.0,对于峰值分离,两个峰值之间深度的50%(见注2)。
5.     运行校准功能以校正和匹配所有样本的峰值,并使用默认选项进行。如果对齐函数不稳定(图8),则使用不同的间隙惩罚选项重新运行校准(Sugimoto等人,2010b)。
 
 
图8。对齐函数的示例。 稳定(左)和不稳定(右)病例。
6.     评估内部标准的重叠峰。如果没有观察到重叠的峰值,请使用不同的选项重新运行对齐(图9)。
 
 
图9。叠加峰值的示例。 成功(左)和不成功(右)结果。
 
7.     运行重新组合函数,根据对齐的峰值矩阵填充缺失的峰值。
8.     将小于定量下限的峰面积换成0。
9.     将产生所有峰的峰面积,并将这些峰面积除以内标物(甲硫氨酸砜用于阳离子,CSA用于阴离子)。
10.  根据校正的迁移时间和标准混合物与样品之间的m/z值,将代谢物命名。
11.  将峰值矩阵导出为CSV文件。
12.  计算唾液代谢物浓度的绝对浓度。
 
 
 
式中,M,IS,STD,PA,V分别表示代谢物、内标物、标准品、峰面积和体积。
 
统计分析
1.     使用JMP进行主成分分析(PCA),以可视化唾液样本中代谢物浓度模式的总体相似性和差异性(见注3)。
2.     使用R进行Mann-Whitney试验,以了解两组之间代谢物浓度的差异。的R函数威尔科克斯检验()使用正确的=F选项(见注4)。
3.     使用错误发现率(Benjamini和Hochberg方法)使用R校正P值,考虑多个独立测试。使用带有method=“fdr”选项的R vector的adjust()方法。
4.     仅选择显示胰腺病人(PC)和非PC组(慢性胰腺炎健康对照组)平均浓度校正P<0.05和折迭变化(F.C.)>4.0的代谢物。
5.     使用JMP为MLR选择最小代谢物集。消除了前向和后向选择的组合(P<0.05)。在JMP上选择[analysis]-[fit model menu],并为个性设置逐步选项。停止规则选择“P值阈值”,方向选择“混合”。输入0.05作为阈值并按下“Go”按钮。
6.     建立多元logistic回归(MLR)模型来区分患者和非PC患者,并计算受试者操作特征曲线(AUC)下的面积。按“运行模型”按钮。
7.     用Weka评价MLR模型的泛化能力。运行k-fold交叉验证(CVs)来评估所开发的MLR的泛化能力。用200个随机值重复CV测试,k=5、10和20(总共600个测试)。跑weka.classifiers.函数.weka包的逻辑等级为–x k(k替换为数值)。
8.     修正AUC值以计算其平均值和95%机密区间。使用GraphPad选择[Analyze Data]-[ROC Curve]菜单。
 
笔记
 
1.     聚丙烯的使用应事先进行评估。利用所提出的方案,我们证实洗脱化合物对唾液代谢物的定量质量没有影响。但是,此确认仅限于我们的标准库。比较了各种储存条件和溶剂(Tomita等人,2018年)。基于观察到的这些数据波动,我们计算产生了唾液代谢物量化水平上的噪声,并评估了唾液生物标记物(Asai等人,2018)对其辨别能力的影响。根据这些数据,应确定操作程序的标准化。
2.     在分析唾液样本之前,应使用标准混合物确定每种代谢物的校准范围。在我们的方案中,我们确认了我们标准库中的代谢物在峰面积和浓度之间表现出高度的线性关系,高达1mm。根据这些数据,确定了定量上限500mm,并预先计算了相应的峰面积。显示大于该阈值的峰面积被视为饱和峰。S/N<1.5的峰被认为是定量下限。
3.     对于PCA分析,只应使用经常观察到的代谢物数据。我们使用每组检测到至少50%的代谢物(Asai等人,2018年),而该阈值取决于样本数量和代谢物数量。
4.     多组的比较取决于研究设计。检测胰腺癌的唾液生物标志物发现研究包括对照组(C)、胰腺癌(PC)和慢性胰腺炎(CP)(Asai等人,2018年)。通常采用三组比较法。在这里,我们的目的是找到高浓度的代谢物只在PC组。因此,我们将C组和CP组作为一组,并使用Mann-Whitney检验比较PC组和(C+CP)组。
 
食谱
 
1.     内标混合物
2毫米蛋氨酸砜
2毫米MES
2毫米CSA
2毫米3-氨基吡咯烷
2毫米Trimesate
2.     护套液
阳离子:甲醇/水(50%v/v),含有0.1 M六烷基(2,2-二氟甲氧基)磷腈μ
阴离子:乙酸铵(5 mmol/L)溶于含有0.1μM六(2,2-二氟甲氧基)磷腈的50%甲醇/水(50%v/v)中
3.     运行缓冲区
阳离子:1 M甲酸
阴离子:50 mM醋酸铵(pH 3.4和pH 8.5)
4.     标准混合物
阳离子:内标200μM,每种代谢物20μM
阴离子:内标200μM,每种代谢物50μM
 
致谢
 
这项研究得到了日本株式会社和日本株式会社开放式研究所的资助。
 
相互竞争的利益
 
没有相互竞争的利益要宣布。
 
 
伦理学
 
这项研究得到了东京医科大学伦理委员会的批准(批准号:1560,2010年9月30日)。从所有患者和同意作为唾液捐赠者的志愿者处获得书面知情同意书。我们的研究是在赫尔辛基宣言之后进行的。
 
工具书类
 
1.     Armitage,E.G.和Ciborowski,M.(2017年)。代谢组学在癌症研究中的应用。高级实验医学生物学965:209-234。
2.     Asai,Y.,Itoi,T.,Sugimoto,M.,Sofuni,A.,Tsuchiya,T.,Tanaka,R.,Tonozuka,R.,Honjo,M.,Mukai,S.,Fujita,M.,Yamamoto,K.,Matsumi,Y.,Kurosawa,T.,Nagakawa,Y.,Kaneko,M.,Ota,S.,Kawachi,S.,Shimazu,M.,Soga,T.,Tomita,M.和Sunamura,M.(2018年)。胰腺癌患者唾液中多胺含量升高。癌症(巴塞尔)10(2)。
3.     Bonne,N.J.和Wong,D.T.(2012年)。利用基因组学、蛋白质组学和代谢组学方法开发唾液生物标志物。基因组医学4(10):82。
4.     Chattopadhayy,I.和Panda,M.(2019年)。口腔癌诊断与治疗的唾液组学生物标志物研究进展。口腔生物学杂志61(2):84-94。
5.     Dawes,C.和Wong,D.T.W.(2019年)。唾液和唾液诊断在促进口腔健康中的作用。牙科研究杂志98(2):133-141。
6.     Ishikawa,S.,Sugimoto,M.,Edamatsu,K.,Sugano,A.,Kitabake,K.和Iino,M.(2020年)。唾液代谢组学鉴别口腔鳞状细胞癌与扁平苔藓。口腔疾病26(1):35-42。
7.     Ishikawa,S.,Sugimoto,M.,Kitabake,K.,Sugano,A.,Nakamura,M.,Kaneko,M.,Ota,S.,Hiwatari,K.,Enomoto,A.,Soga,T.,Tomita,M.和Iino,M.(2016年)。口腔癌筛查用唾液代谢组学生物标志物的鉴定。Sci报告6:31520。
8.     Ishikawa,S.,Sugimoto,M.,Kitabake,K.,Tu,M.,Sugano,A.,Yamamori,I.,Iba,A.,Yusa,K.,Kaneko,M.,Ota,S.,Hiwatari,K.,Enomoto,A.,Masaru,T.和Iino,M.(2017年)。唾液代谢生物标志物采集时机对口腔癌检测的影响。氨基酸49(4):761-770。
9.     Kaczor Urbanowicz,K.E.,Martin Carreras Presas,C.,Aro,K.,Tu,M.,Garcia Godoy,F.和Wong,D.T.(2017年)。唾液诊断-当前观点和方向。实验生物医学(梅伍德)242(5):459-472。
10.  Martina,E.,Campanati,A.,Diotallevi,F.和Offidani,A.(2020年)。唾液和口腔疾病。临床医学杂志9(2)。
11.  Mikkonen,J.J.,Singh,S.P.,Herrala,M.,Lappalinen,R.,Myllyma,S.和Kullaa,A.M.(2016年)。唾液代谢组学在口腔癌和牙周病诊断中的应用。牙周病学研究杂志51(4):431-437。
12.  Monton,M.,R.,和Soga,T.(2007年)。代谢组分析毛细管电泳质谱。色谱杂志A 168(1-2):237-246。
13.  Murata,T.,Yanagisawa,T.,Kurihara,T.,Kaneko,M.,Ota,S.,Enomoto,A.,Tomita,M.,Sunamura,M.,Hayashida,T.,Kitagawa,Y.和Jinno,H.(2019年)。唾液代谢组学与基于决策树的机器学习方法鉴别乳腺癌。乳腺癌研究治疗177(3):591-601。
14.  Soga,T.,Ohashi,Y.,上野,Y.,Naraoka,H.,Tomita,M.和Nishioka,T.(2003年)。毛细管电泳-质谱法定量分析代谢组。蛋白质组学研究2(5):488-494。
15.  Soga,T.(2007年)。代谢组学毛细管电泳质谱法。方法分子生物学358:129-137。
16.  Sridharan,G.,Ramani,P.,Patankar,S.和Vijayaraghavan,R.(2019年)。口腔白斑和口腔鳞状细胞癌唾液代谢组学评价。口腔病理医学杂志48(4):299-306。
17.  杉本,M.,Wong,D.T.,平山,A.,Soga,T.和Tomita,M.(2010a)。以毛细管电泳质谱为基础的唾液代谢组学鉴定了口腔癌、乳腺癌和胰腺癌的特异性特征。代谢组学6(1):78-95。
18.  杉本,M.,川崎,M.,罗伯特,M.,Soga,T.,Tomita和M.(2012年)。基于质谱的代谢组数据处理和分析的生物信息学工具。当前生物信息7(1):96-108。
19.  Sugimoto,M.,Saruta,J.,Matsuki,C.,To,M.,Onuma,H.,Kaneko,M.,Soga,T.,Tomita,M.和Tsukinoki,K.(2013年)。与基于质谱的唾液代谢组学特征相关的生理和环境参数。代谢组学9(2):454-463。
20.  杉本,M.,平山,A.,石川,T.,罗伯特,M.,Baran,R.,Uehara,K.,Kawai,K.,Soga,T.和Tomita,M.(2010b)。用于毛细管电泳质谱数据分析的微分代谢组学软件。代谢组学6(1):27-41。
21.  Takayama,T.,Tsutsui,H.,Shimizu,I.,Toyama,T.,Yoshimoto,N.,Endo,Y.,Inoue,K.,Todoroki,K.,Min,J.Z.,Mizuno,H.和Toyo&apos;oka,T.(2016年)。基于唾液靶代谢组学的乳腺癌诊断方法。临床学报452:18-26。
22.  Tomita,A.,Mori,M.,Hiwatari,K.,Yamaguchi,E.,Itoi,T.,Sunamura,M.,Soga,T.,Tomita,M.,Sugimoto,M.(2018年)。储存条件对液相色谱-质谱法定量唾液多胺的影响。科学报告8(1):12075。
23.  Trezzi,J.P.,Vlassis,N.和Hiller,K.(2015年)。代谢组学在癌症生物标志物研究和诊断工具开发中的作用。生物医药第57-867期。
24.  Wang,Q.,Gao,P.,Wang,X.和Duan,Y.(2014年)。口腔鳞状细胞癌早期诊断潜在生物标志物的研究与鉴定。临床学报427:79-85。
25.  Wang,X.,Kaczor Urbanowicz,K.E.和Wong,D.T.(2017年)。唾液生物标志物在癌症检测中的应用。肿瘤学34(1):7。
26.  沃堡,O.(1956年)。癌症细胞呼吸障碍的研究。科学124(3215):269-270。
27.  Washio,J.和Takahashi,N.(2016年)。口腔生物膜、口腔癌等的代谢组学研究。《国际分子科学杂志》17(6)。
28.  Yoshizawa,J.M.,Schafer,C.A.,Schafer,J.J.,Farrell,J.J.,Paster,B.J.和Wong,D.T.(2013年)。唾液生物标志物:未来临床和诊断应用。临床微生物学26(4):781-791。
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引用:Sugimoto, M., Ota, S., Kaneko, M., Enomoto, A. and Soga, T. (2020). Quantification of Salivary Charged Metabolites Using Capillary Electrophoresis Time-of-flight-mass Spectrometry. Bio-protocol 10(20): e3797. DOI: 10.21769/BioProtoc.3797.
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