High-flux metabolite detection method and application thereof

Abstract

The invention relates to the technical field of metabolite detection, in particular to a high-flux metabolite detection method and application thereof. According to the detection method, metabolites in a biological sample to be detected are detected by using a liquid chromatography-mass spectrometry technology, wherein mass spectrometry is quadrupole electrostatic field orbitrap high-resolution and triple quadrupole mass spectrometry; the mass spectrum conditions of the MRM ion pair are as follows: acquiring data in an NFSWI-DDA, NFSWI-DIA or DIA mode in a positive and negative ion mode, and acquiring and annotating the MRM ion pair; the MRM list is transited to triple quadrupole mass spectrometry, and the mass spectrometry conditions of the triple quadrupole mass spectrometry include verification and detection of MRM ion pairs in positive and negative ion modes. The detection method can meet the requirements of precise medical research on metabolite coverage rate, quantitative stability and detection flux, can be used for semi-quantitative and quantitative combined detection of metabolites in various biological samples, has applicability, and can provide technical support for disease diagnosis, drug development and biomarker discovery.

Description

Technical Field

[0001] The present invention relates to the technical field of metabolite detection, and in particular to a high-throughput metabolite detection method and application thereof. Background Art

[0002] Metabolomics detects various physiological metabolites and circulating metabolites in organisms, facilitating intuitive analysis of pathophysiological changes. This technology makes it possible to study disease mechanisms and early diagnosis, positioning it as a core technology for future precision medicine. Large-scale metabolomics studies place strict demands on analytical methods, especially in terms of metabolite coverage, quantitative stability, and detection throughput. Although HRMS-based non-targeted metabolomics allows unbiased detection of as many metabolites as possible in biological samples, it usually provides relative concentrations of metabolites rather than absolute concentrations. Differently, TQ-MS-based targeted metabolomics emphasizes a specific list of known metabolites, which can be further quantified using calibration curves, making it an ideal tool for obtaining metabolite quantitative data. However, its main disadvantage is its limited coverage of metabolites.

[0003] Quasi-targeted metabolomics is a new omics strategy that combines the advantages of untargeted metabolomics and targeted metabolomics: In the development of large-scale targeted metabolomics methods, HRMS is usually used to obtain MS 2 Information (step 1), high coverage dynamic MRM analysis using TQ-MS (step 2). MS acquired based on non-targeted metabolomics analysis 2 The construction and identification of MRM ion pair data sets are the basis for establishing large-scale targeted metabolomics methods. 2 An innovative data collection mode with wide coverage, reliable identification and good repeatability is used to establish a high-throughput targeted metabolomics method. Summary of the invention

[0004] In view of the above technical problems, the present invention provides a high-throughput metabolite detection method and its application. The high-throughput metabolite detection method provided by the present invention can meet the requirements of precision medicine research for wide metabolite coverage, stable quantitative analysis, and large detection flux, and can be used for high-throughput detection of metabolites in a variety of biological samples, and can also be further used for metabolite identification and metabolic pathway analysis.

[0005] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a high-throughput metabolite detection method, which uses liquid chromatography-mass spectrometry to detect metabolites in a biological sample to be tested, wherein the mass spectrometer is a quadrupole electrostatic field orbital trap high-resolution mass spectrometer and a triple quadrupole mass spectrometer; the mass spectrometry conditions of the high-resolution quadrupole mass spectrometer include: collecting HRMS data in positive and negative ion modes in non-fixed segmented window interval data dependent acquisition (NFSWI-DDA), non-fixed segmented window interval data independent acquisition (NFSWI-DIA) or conventional data independent acquisition (DIA) mode, and acquiring and annotating MRM ion pairs; the MRM list is transitioned to a triple quadrupole mass spectrometer, and the mass spectrometry conditions of the triple quadrupole mass spectrometer include: verifying and detecting MRM ion pairs in positive and negative ion modes.

[0007] The high-throughput metabolite detection method provided by the present invention is based on an innovative MS 2 Quasi-targeted metabolomics method for data acquisition mode establishment, MS 2 High coverage, good quantitative precision, MS 2 The data is reliable and qualitatively accurate, providing a more convenient platform for high-throughput metabolome analysis, and can be used for quantitative analysis of metabolites and research on overall metabolic changes in organisms, with universal applicability. The methodological investigation shows that the above-mentioned pseudo-targeted metabolome method established by the present invention meets all the requirements of methodology. It can be seen from the application of metabolite detection in mouse plasma and liver that the method has good applicability.

[0008] In combination with the first aspect, the high-resolution quadrupole mass spectrometer preferably uses NFSWI-DDA mode to collect data. The present invention has found through experimental research that the NFSWI-DDA mode can obtain effective MS equivalent to the NFSWI-DIA mode. 2 The information abundance was higher than that of the control group, while the quantitative precision was not significantly different. 2 The qualitative reliability of the model is significantly higher than that of the NFSWI-DIA model. 2 Low Abundance and DIA Mode MS 2 The NFSWI-DDA mode has the advantages of high MS coverage, good precision, high spectral quality and convenient data processing. 2 Reliable data, accurate qualitative analysis, and DIA mode MS 2 It has the characteristics of high coverage and good quantitative precision.

[0009] In combination with the first aspect, the biological sample to be tested includes a pretreated blood sample or a liver sample; wherein the operation of pretreating the blood sample includes: mixing the blood sample with an internal standard solution and a solvent for precipitating protein, centrifuging after ice-water bath ultrasonic treatment, taking the supernatant, blowing dry with nitrogen, re-dissolving before detection, centrifuging, and taking the supernatant; the operation of pretreating the liver sample includes: homogenizing the liver sample with physiological saline to obtain a homogenate, mixing the homogenate with an internal standard solution and a solvent for precipitating protein, centrifuging after ice-water bath ultrasonic treatment, taking the supernatant, blowing dry with nitrogen, re-dissolving before detection, centrifuging, and taking the supernatant; the solvent for precipitating protein is 25% to 100% v / v methanol aqueous solution, 50% to 75% v / v methanol acetonitrile solution or acetonitrile, and the solvent for re-dissolving is 25% to 100% v / v methanol aqueous solution, 50% to 75% v / v methanol acetonitrile solution or acetonitrile.

[0010] Preferably, the solvent for precipitating the protein is a 50% v / v methanol-acetonitrile solution, and the solvent for re-dissolving is a 75% v / v methanol-acetonitrile solution or a 75% v / v methanol-water solution. When the protein is precipitated with a 50% v / v methanol-acetonitrile solution, the minimum response value and the response value of the low-content component are both high, and when the protein is re-dissolved with a 75% v / v methanol-acetonitrile solution or a 75% v / v methanol-water solution, the extraction characteristic peak response ratio, total response value, maximum value and minimum value are all good.

[0011] In combination with the first aspect, when the mass spectrometry is the mass spectrometry condition of the high-resolution quadrupole mass spectrometry, the liquid chromatography-mass spectrometry technology includes liquid chromatography-mass spectrometry under reverse phase chromatography column conditions and liquid chromatography-mass spectrometry under amino chromatography column conditions.

[0012] Preferably, in the LC-MS under the reverse phase chromatographic column conditions, the chromatographic conditions include:

[0013] The chromatographic column is CSH C 18 Chromatographic columns;

[0014] Mobile phase A was acetonitrile-methanol in a volume ratio of 1:1, and mobile phase B was water containing 5 mM ammonium formate, and gradient elution was performed. The program of the gradient elution was: 0-1 min: 99% B; 1-3 min: 99% B-50% B; 3-8 min: 50% B-1% B; 8-9 min: 1% B;

[0015] The column temperature was 35-45°C; the flow rate was 0.3 mL / min;

[0016] The mass spectrometry conditions include: obtaining the abundance of the characteristic peak Q1 of the biological sample in the full scan mode, MS 1The full scan range is 100 to 1000, and the resolution is 120000; data is then collected in NFSWI-DDA or NFSWI-DIA mode.

[0017] Preferably, in the reverse phase liquid chromatography conditions, the chromatographic column is UPLC CSH TM C 18 (Waters, 2.1×100mm, 1.7μm);

[0018] Preferably, the mass spectrometry conditions also include: an electrospray ion source, an auxiliary gas flow rate of 7Arb, a sheath gas flow rate of 35Arb, a capillary temperature of 320°C, a nebulizer gas temperature of 420°C, a positive ion spray voltage of 3.50 kV, a negative ion spray voltage of 2.80 kV, and collision energy NCE is set to 10, 20, 30, 40 and 50 V, respectively.

[0019] Preferably, in the LC-MS under the amino chromatographic column conditions, the chromatographic conditions include:

[0020] The chromatographic column is a BEH Amide column;

[0021] Mobile phase A is acetonitrile-water with a volume ratio of 95:5 and contains 5 mM ammonium formate, and mobile phase B is water-acetonitrile with a volume ratio of 95:5 and contains 5 mM ammonium formate; gradient elution is performed, and the program of the gradient elution is: 0-17 min: 5% B-40% B; 17-20 min: 40% B; 20-23 min: 40% B-5% B;

[0022] The column temperature was 45°C; the flow rate was 0.2 mL / min;

[0023] Mass spectrometry conditions include: Full-ddMS 2 Mode data acquisition, Full scan resolution: 60000, ddMS 2 Resolution: 30000; electrospray ion source; auxiliary gas flow rate is 7Arb; sheath gas flow rate is 35Arb; capillary temperature is 320℃; nebulizer gas temperature is 420℃; spray voltage is 3.50kV (POS) or 2.80kV (NEG); collision energy NCE is 15 / 30 / 45V.

[0024] In combination with the first aspect, when the mass spectrometer is a triple quadrupole mass spectrometer, the liquid chromatography-mass spectrometry technology includes liquid chromatography-mass spectrometry under reverse phase chromatography column conditions and liquid chromatography-mass spectrometry under amino chromatography column conditions.

[0025] Preferably, in the LC-MS under the reverse phase chromatographic column conditions, the chromatographic conditions include:

[0026] The chromatographic column is CSH C18 Chromatographic columns;

[0027] Mobile phase A was acetonitrile-methanol in a volume ratio of 1:1, and mobile phase B was water containing 5 mM ammonium formate, and gradient elution was performed, and the program of the gradient elution was: 0-4 min: 99% B-97% B; 4-7 min: 97% B-80% B; 7-16 min: 80% B-0% B; 16-18 min: 0% B;

[0028] The column temperature was 40-50°C; the flow rate was 0.5 mL / min;

[0029] The mass spectrometry conditions include: positive ion and negative ion mode detection, respectively; capillary voltage: +3.5kV / -2.8kV; cone voltage: 30V; capillary temperature: 500°C; desolvation temperature: 450°C; desolvation gas flow rate of 1000L / Hr; 1000L / Hr; auxiliary gas: 50L / Hr, and the detection mode adopts MRM mode.

[0030] Preferably, in the LC-MS under the amino chromatographic column conditions, the chromatographic conditions include:

[0031] The chromatographic column is a BEH Amide column;

[0032] Mobile phase A is acetonitrile-water with a volume ratio of 95:5 and contains 5 mM ammonium formate, and mobile phase B is water-acetonitrile with a volume ratio of 95:5 and contains 5 mM ammonium formate; gradient elution is performed, and the program of the gradient elution is: 0-17 min: 5% B-40% B; 17-20 min: 40% B; 20-23 min: 40% B-5% B;

[0033] The column temperature was 45°C; the flow rate was 0.2 mL / min;

[0034] The mass spectrometry conditions include: positive ion and negative ion mode detection, respectively; capillary voltage: +3.5kV / -2.8kV; cone voltage: 30V; capillary temperature: 500°C; desolvation temperature: 450°C; desolvation gas flow rate of 1000L / Hr; 1000L / Hr; auxiliary gas: 50L / Hr, and the detection mode adopts MRM mode.

[0035] In combination with the first aspect, the chromatographic column is UPLC CSH TM C 18 , ACQUITY UPLC BEH Amide, LunaOmega Polar C 18 or CORTECS UPLC T3. The characteristic peak cumulative response values ​​obtained by using the above chromatographic columns are higher and can better meet the detection needs. TMC 18 Reversed-phase chromatography columns have not been reported in metabolomics studies.

[0036] The beneficial effects of the present invention are as follows: the present invention combines the advantages of DDA and DIA with a non-fixed segmented window interval data-dependent acquisition strategy, which is generally applicable to HRMS mass spectrometers, reduces the instrument limitations of the quasi-targeted method, and also provides a new method for MS 2 The information acquisition mode provides a new perspective. The quantitative and semi-quantitative quasi-targeted metabolomics method based on the NFSWI-DDA model of the present invention can meet the requirements of precision medicine research on metabolite coverage, quantitative stability and detection flux, and can be used for the detection of metabolites (including changed and unchanged metabolites) in a variety of biological samples. It has applicability and will provide technical support for disease diagnosis, drug development, and biomarker discovery. After testing, the quasi-targeted metabolomics method established based on this can detect a total of 3002 MRM ion pairs, including 1633 positive ion modes, 1369 negative ion modes, and 475 annotated metabolites (of which 327 metabolites are absolutely quantitative), and has good linearity, precision, repeatability, recovery rate, matrix effect and stability, which can be used for the relative quantitative analysis of 475 metabolites. These metabolites cover 9 major metabolic pathways, including 65 secondary metabolic pathways, which can reflect the basic metabolic processes in the body. Therefore, this detection method can be used for the identification of metabolites and metabolic pathway analysis. The application analysis results showed that the QC samples of plasma and liver of the normal group and lipopolysaccharide (LPS)-induced liver injury model group were closely clustered together, and the detection rates of metabolites in mouse plasma and liver were both over 400, indicating that this method is stable and suitable for the detection of a variety of biological samples. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is the annotation of the MRM ion pairs in Example 1 of the present invention and the comparison with the standard product;

[0038] Figure 2 is the KEGG pathway involved in the MRM ion pairs annotated and compared with the standard sample in Example 1 of the present invention;

[0039] Figure 3 The MRM transition set of quality control samples is generated based on the NFSWI-DDA mode in Example 1 of the present invention;

[0040] Figure 4 The mixed QC sample in Example 1 of the present invention is BEH Amide and CSH C 18 MRM chromatogram in the column;

[0041] Figure 5 It is the response proportion, total response value, maximum value and minimum value of the characteristic peaks extracted under the seven extraction solvents in Test Example 1 of the present invention;

[0042] Figure 6 It is the response proportion, total response value, maximum value and minimum value of the characteristic peaks extracted under the four re-dissolving solvents in Test Example 1 of the present invention;

[0043] Figure 7 is the number of characteristic peaks and cumulative response values ​​of the Pooled quality control samples in Test Example 2 of the present invention on different chromatographic columns;

[0044] Figure 8 The UPLC CSH TM C 18 RT distribution of metabolites under different chromatographic columns and mobile phase conditions (Figure A), chromatographic peak scores (Figure B), and average characteristic peak responses (Figure C). The chromatographic peak score criteria are: 3: good peak shape; 2: good peak shape with impurities; 1: quantifiable with tailing or interference peaks; 0: unquantifiable with severe tailing or interference;

[0045] Fig. 9 It is the optimization (POS) of the HRMS Q1 segmented window in Test Example 4 of the present invention: (A) TIC of the fixed Q1 segmented window mode; (B) TIC of the non-fixed Q1 segmented window mode calculated according to SwathTUNER. The numbers in brackets in the figure indicate the number of isolation windows of Q1 (full scan: 70-1000);

[0046] Fig.10 It is the optimization (NEG) of the HRMS Q1 segmented window in Test Example 4 of the present invention: (A) TIC collected by fixed SWATH window; (B) TIC collected by non-fixed SWATH window calculated according to SwathTUNER. The numbers in brackets in the figure indicate the number of isolation windows of FullScan 70-1000;

[0047] Fig.11 The HRMS MS data obtained by different acquisition modes in the test example 4 of the present invention (conventional data dependent acquisition (DDA), fixed segmented window data dependent acquisition (FSWI-DDA), fixed segmented window interval data dependent acquisition (FSWI-DDA), non-fixed segmented window data dependent acquisition (NFSW-DDA), non-fixed segmented window interval data dependent acquisition (NFSWI-DDA), conventional data independent acquisition (DIA) and non-fixed segmented window interval data independent acquisition (NFSWI-DIA)) 2 Information comparison: (A)MS 2 Information abundance; (B) MS 2 Quantitative precision; (C)MS 2 The percentage of spectrum point integral score;

[0048] Fig.12It is the linearity (A), intra-day precision (B), inter-day precision (C) and repeatability (D) of the pseudo-targeted metabolomics method in Test Example 5 of the present invention (Y-axis "Number" refers to the number of metabolites that meet the characteristic conditions; Y-axis "Percentage" refers to the percentage of metabolites that meet the characteristic conditions in the total number of metabolites);

[0049] Fig.13 It is the sample recovery rate (A), matrix effect (B), freeze-thaw stability (C), and long-term stability (D) of the quantitative analysis of the intended targeted metabolites in Test Example 5 of the present invention (the Y-axis "Number" refers to the number of metabolites that meet the characteristic conditions; the Y-axis "Percentage" refers to the percentage of metabolites that meet the characteristic conditions in the total number of metabolites);

[0050] Fig.14 This is the pseudo-metabolomic application analysis of LPS-induced liver injury mice in Example 12 of the present invention (plasma: A, B, C; liver: D, E, F); Figures A and D are PCA diagrams of the LPS vs Control group, (Figures B and E are the variability of QC samples, and Figures C and F are the verification results of the OPLS-DA model. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work fall within the scope of protection of the present invention.

[0052] Metabolites have huge structural diversity and a wide range of concentrations. Therefore, metabolomics requires powerful analytical techniques with broad coverage, high sensitivity and specificity, and a wide dynamic range. Quasi-targeted metabolomics combines the advantages of non-targeted metabolomics and targeted metabolomics. The acquisition and identification of MRM ion pairs is the basis for establishing quasi-targeted metabolomics methods, which enriches reliable MS 2 The acquisition of information is crucial. 1 Acquisition Mode and MS 2 It is necessary to optimize the acquisition mode.

[0053] The present invention aims to develop a MS with both DDA mode 2 Reliable data, accurate qualitative analysis, and DIA mode MS 2 A data acquisition mode with high coverage and good quantitative precision is developed, and a quasi-targeted metabolomics method is established based on this, which is applied to the quantitative and semi-quantitative analysis of the metabolic profiles of biological samples.

[0054] In view of the above invention objectives, an embodiment of the present invention provides a high-throughput metabolite detection method, which uses liquid chromatography-mass spectrometry to detect metabolites in a biological sample to be tested, and the mass spectrometry is a quadrupole electrostatic field orbital trap high-resolution mass spectrometry and a triple quadrupole mass spectrometry; the mass spectrometry conditions of the high-resolution quadrupole mass spectrometry include: collecting HRMS data in positive and negative ion modes in NFSWI-DDA, NFSWI-DIA or DIA mode, and obtaining and annotating MRM ion pairs; the MRM list is transitioned to a triple quadrupole mass spectrometry, and the mass spectrometry conditions of the triple quadrupole mass spectrometry include: verifying and detecting MRM ion pairs in MRM mode in positive and negative ion modes.

[0055] The embodiments of the present invention also provide the application of the high-throughput metabolite detection method in the characterization and identification of plasma metabolites.

[0056] The following is further described with reference to specific embodiments.

[0057] The reagents and drugs used in the following examples are:

[0058] HPLC-grade methanol, acetonitrile, and isopropanol (Thermo Fisher, USA); MS-grade formic acid (Thermo Fisher, USA); ammonium formate and ammonium acetate (Sigma, USA); and ultrapure water were purchased from Watsons Food & Beverage Co., Ltd. (China); a total of 327 standards including endogenous metabolites tryptophan, serine, xanthine nucleoside, adenosine, cytidine, nicotinamide, creatine, lactic acid, lauric acid, cholesterol, and ursodeoxycholic acid were purchased from Shanghai Yuanye Biotechnology Co., Ltd., Sigma-Aldrich, and Avanti Polar Lipids, USA; 12 isotopic internal standards L-glutamine- 13 C5, glucose- 13 C, succinic acid-d6, palmitic acid-d 31 , bile acid-d4, benzoic acid-d5, lauric acid-d 23 , L-phenylalanine-d8, palmitoyl-L-carnitine-d3, L-carnitine-d9, L-glutamic acid- 15 N and hippuric acid-d2 standards were purchased from Shanghai Yuanye Biotechnology Co., Ltd. and Sigma-Aldrich Company; lipopolysaccharide was purchased from Sigma-Aldrich Company.

[0059] The main instruments and equipment used in the following examples are:

[0060] UHPLC-Orbitrap Exploris 120 system (Thermo Fisher Scientific, USA), TQ-S Micro MS system (Waters Corporation, USA).

[0061] Example 1

[0062] This embodiment provides a high-throughput metabolite detection method.

[0063] 1. Experimental animals and groups

[0064] Twenty male 8-week-old C57BL / 6 mice (18-22g), SPF grade, were purchased from Beijing Huafukang Biotechnology Co., Ltd. They were kept in the Animal Experiment Center of Tianjin University of Traditional Chinese Medicine in accordance with the animal husbandry and animal laboratory management standards, with a room temperature of 25±2℃ and a relative humidity of 45%-60%. The experiment began after one week of adaptive feeding, with light and dark cycles every 12h, free access to food and water, and fasting for 12h before the experiment. After one week of adaptive feeding, the 20 mice were randomly divided into two groups: a normal group and an LPS group, with 10 mice in each group. The LPS group was intraperitoneally injected with 5mg / kg LPS saline solution, and the normal group was intraperitoneally injected with an equal volume of saline.

[0065] 2. Preparation of standard solution and internal standard solution

[0066] Accurately weigh 1 mg of each of 327 metabolites including methionine, glutathione, cytosine, pantothenic acid, malic acid, shikimic acid, histidine, etc., and place them in 1 mL volumetric flasks. Add appropriate amount of appropriate solvent (methanol, 50% methanol water, acetonitrile, DMSO, etc.) to completely dissolve the standard, and then add methanol to make up to 1 mg / mL mother solution. The mother solutions of the standards are grouped according to the category and mass number MW value, such as standard solutions of amino acids, nucleotides, choline, bile acids, fatty acids, etc. Since the same group of compounds contains metabolites with the same mass number MW, there may be more than one mixed standard solution for one class of compounds. For example, there are three mixed standard solutions A, B, and C for amino acids, and two mixed standard solutions A and B for nucleotides. Accurately aspirate the corresponding volume of single-standard mother solution, mix them, and add 75% methanol water to make up to the concentration of each metabolite to 5 μg / mL according to the metabolite name of each mixed standard solution, and set aside. Accurately weigh L-glutamine- 13 C5, glucose- 13 C, succinic acid-d6, palmitic acid-d 31 1 mg of each of the 12 isotopic internal standards was placed in a 1.5 mL centrifuge tube, and 1 mL of 75% methanol aqueous solution was added to prepare a 1 mg / mL mother solution. The mother solution was diluted with 75% methanol aqueous solution to the required concentration before injection.

[0067] 3. Biological sample collection

[0068] Collection of human plasma samples: 46 normal human blood samples were collected from Jinghai Hospital, Tianjin, in sodium heparin tubes, centrifuged at 4000 rpm for 20 min at 4°C, and the supernatant plasma was transferred to a clean centrifuge tube and placed at -80°C for use. Human plasma samples were obtained from healthy volunteers and the volunteers agreed to the samples. Equal volumes of human plasma samples were taken and mixed in turn to prepare PooledQC quality control samples for the establishment of the targeted metabolomics method.

[0069] Collection of mouse plasma and liver samples: 20 mice were anesthetized 4 hours after intraperitoneal injection, blood was collected from the inner canthus of the orbit, placed in an EP tube pre-treated with sodium heparin, and centrifuged at 7000rpm for 20min at 4°C. The supernatant plasma was transferred to a clean centrifuge tube and placed at -80°C for use; after dissection, liver samples were collected and stored at -80°C for future use.

[0070] 4. Biological sample preparation

[0071] Plasma samples: First, take 100 μL of plasma sample in a 1.5 mL EP tube, add 50 μL of isotope internal standard solution and 350 μL of ice methanol / acetonitrile (1:1), vortex for 2 min, then ultrasonicate in an ice water bath for 10 min, centrifuge at 14000 rpm for 15 min at 4 ° C, then take the supernatant, blow dry with nitrogen, and store in a -80 ° C refrigerator. Before injection, reconstitute with 100 μL of 75% v / v methanol aqueous solution, vortex for 30 s, centrifuge at 14000 rpm for 15 min at 4 ° C, and take 85 μL of supernatant into the injection vial.

[0072] Liver sample: Weigh 50 mg of liver tissue, add 200 μL of 4°C physiological saline for tissue homogenization, add 100 μL of internal standard solution and 500 μL of ice methanol / acetonitrile (1:1, v:v) to the homogenate, vortex for 5 min, then ultrasonicate in an ice water bath for 10 min, centrifuge at 14000 rpm for 15 min at 4°C, then take the supernatant, blow dry with nitrogen, and store in a -80°C refrigerator. Before injection, reconstitute with 100 μL of 75% v / v methanol aqueous solution, vortex for 30 s, centrifuge at 14000 rpm for 15 min at 4°C, and take 85 μL of the supernatant into an injection vial.

[0073] 5. HRMS HPLC Conditions

[0074] 5.1 LC-MS conditions for reversed-phase column CSH

[0075] MS acquired using UHPLC-Orbitrap Exploris 120 high-resolution mass spectrometry HRMS platform 1 and MS 2 Information construction and annotation of MRM transitions.

[0076] Chromatographic conditions: Column: UPLC CSH TMC 18 (Waters, 2.1×100 mm, 1.7 μm); mobile phase: (A) acetonitrile / methanol (1:1, v:v)-(B) water (containing 5 mM ammonium formate); elution gradient as follows: 0-1 min: 99% B; 1-3 min: 99% B-50% B; 3-8 min: 50% B-1% B; 8-9 min: 1% B; 9-9.1 min: 99% B; 9.1-12 min: 99% B. Column temperature: 40°C; flow rate: 0.3 mL / min; injection volume: 2 μL.

[0077] Mass spectrometry conditions: Obtain the abundance of characteristic peak Q1 of biological samples in Full Scan mode and optimize MS 1 The number of segmented windows and the m / z range (i.e., the segmented strategy for non-fixed window acquisition) are used. In this case, positive and negative ions are scanned simultaneously, and the MS 1 The full scan range was 100 to 1000, and the resolution was 120000. Afterwards, under the optimized segmentation strategy of non-fixed window acquisition, data were collected in NFSWI-DDA mode, and data in positive and negative ion modes were collected separately. In both positive and negative ion modes, data were collected in segmented intervals, so that all MS of the sample was obtained. 2 The information needs to be collected twice for positive ion data and twice for negative ion data. The MS 1 and MS 2 The m / z range is shown in Table 1. The other parameters of mass spectrometry acquisition were set as follows: electrospray ion source (HESI source), auxiliary gas flow rate of 7Arb, sheath gas flow rate of 35Arb, capillary temperature of 320℃, nebulizer gas temperature of 420℃, spray voltage of 3.50kV (positive ion) or 2.80kV (negative ion). The collision energy NCE was set to 10, 20, 30, 40 and 50V, respectively, and MS was collected at different collision energies. 2 information to obtain the optimal NCE value for MRM.

[0078] Table 1 Segmentation strategy for non-fixed window acquisition

[0079]

[0080] 5.2 LC-MS conditions under BEH amino column

[0081] Chromatographic conditions: Chromatographic column: ACQUITY UPLC BEH Amide (Waters, 2.1×100 mm, 1.7 μm); Mobile phase: (A) acetonitrile / water (95:5, v:v) (containing 5 mM ammonium formate)-(B) water / acetonitrile (95:5, v:v) (containing 5 mM ammonium formate); Elution gradient as follows: 0-17 min: 5% B-40% B; 17-20 min: 40% B; 20-23 min: 40% B-5% B; 23-27 min: 5% B. Flow rate: 0.2 mL / min; Column temperature: 45°C; Injection volume: 2 μL.

[0082] Mass spectrometry conditions: Full-ddMS 2 Mode data acquisition, Full scan resolution: 60000, ddMS 2 Resolution: 30000; electrospray ion source (HESI source); auxiliary gas flow rate is 7Arb; sheath gas flow rate is 35Arb; capillary temperature is 320℃; nebulizer gas temperature is 420℃; spray voltage is 3.50kV (POS) or 2.80kV (NEG); collision energy NCE is 15 / 30 / 45V.

[0083] 6. TQ-MS HPLC Conditions

[0084] 6.1 LC-MS conditions for reversed-phase column CSH

[0085] In this experiment, the TQ-S Micro MS system was used to detect the targeted metabolites.

[0086] Chromatographic conditions: Column: UPLC CSH TM C 18 (Waters 2.1×100mm, 1.7μm); mobile phase: (A) acetonitrile / methanol (1:1, v:v)-(B) water (containing 5mM ammonium formate); elution gradient as follows: 0-4min: 99% B-97% B; 4-7min: 97% B-80% B; 7-16min: 80% B-0% B; 16-18min: 0% B; 18-20min: 99% B. Column temperature: 45°C; flow rate: 0.5mL / min; injection volume: 4μL.

[0087] Mass spectrometry conditions: positive ion and negative ion modes were detected separately; capillary voltage: +3.5kV / -2.8kV; cone voltage: 30V; capillary temperature: 500℃; desolvation temperature: 450℃; desolvation gas flow rate was 1000L / Hr (N2, purity>99.9%); 1000L / Hr; auxiliary gas: 50L / Hr, and the detection mode adopted was MRM mode.

[0088] 6.2 LC-MS conditions under BEH amino column

[0089] The chromatographic conditions are the same as those in "5.2 LC-MS conditions for BEH amino chromatographic column". The mass spectrometry conditions are the same as those in "6.1 LC-MS conditions for reversed-phase chromatographic column CSH".

[0090] 7. Data processing

[0091] 7.1 HRMS data processing

[0092] Subtract the background interference (blank solvent) list collected in Full Scan acquisition mode to obtain accurate and effective MS of biological samples 2 The “Polarity switch” process was run using the xcmsGUI tool to obtain the RT and response of the characteristic peak Q1. The original Raw. format data obtained by the HRMS system was then converted into an mgf. file supported by XCMS using the MSConvert tool provided by ProteoWizard. The data was then analyzed using XCMS (http: / / www.bioconductor.org / packages / release / bioc / html / xcms.html) and CAMER based on R software.

[0093] The (http: / / www.bioconductor.org / packages / release / bioc / html / CAMERA.html) installation package is used for peak detection and peak annotation; the MRM_Ion_Pair_Finder (https: / / github.com / zhengfj1994 / MRMIon_Pair_Finder / tree / master / R) installation package is used to obtain MRM ion pairs.

[0094] In the process of MRM ion pair annotation, Compound Discover 3.3, MSDIAL, MS Finder, PCDL software and MetDNA2 analysis were used, combined with HMDB, Pubchem, Dugbank, Metlin, MzCloud and other databases, Pubmed literature was consulted, and the obtained MRM ion pairs were identified and annotated by comparing with the standard. Results (see Figure 1), a total of 475 MRM ion pairs were annotated, including 78 nucleotides, 61 amino acids, 12 cholines, 42 bile acids, 42 fatty acids, 15 carbohydrates, 15 carnitines, 89 organic acids, 36 lipids, and 85 others; 327 standard samples were compared, including 59 nucleotides, 49 amino acids, 10 cholines, 39 bile acids, 27 fatty acids, 9 carbohydrates, 5 carnitine compounds, 63 organic acids, 4 lipids, and 62 others. The reaction pathways involved basically include basic physiological processes such as sugar metabolism, lipid metabolism, amino acid metabolism, nucleotide metabolism, and energy metabolism, which can basically reflect the biological metabolism of the body. The KEGG pathway diagram is shown in Figure 2 .

[0095] 7.2 TQ-MS data processing

[0096] This experiment found that the NCE value of HRMS and the CE value of TQ-MS were very different. Based on the existing standards in the laboratory, the optimal NCE values ​​of multiple and multi-category compounds on HRMS and the optimal CE values ​​on TQ-MS were compared, and it was found that the CE value on TQ-MS basically corresponded to half of the NCE value on OE. Therefore, the CE optimization range of MRM was set: 1 / 2NCE-6, 1 / 2NCE-3, 1 / 2NCE, 1 / 2NCE+3, 1 / 2NCE+6, and the optimal CE value was screened based on Skyline software.

[0097] Mass spectrometry data were collected using Masslynx 4.1. Skyline software optimized the CE value of each MRM ion pair, and the MRM_Ion_Pair_Finder package was used to calibrate the t values ​​of all MRM ion pairs based on the internal standard and standard information. R . In the targeted metabolomics analysis, TargetLynx software was used to correct the peak area of ​​each metabolite according to the internal standard. Metabolites that were detectable in less than 80% of the samples in each sample group and metabolites with a CV value greater than 30% in the QC samples were removed. The pre-processed targeted metabolomics data matrix was subjected to multivariate statistical analysis (principal component analysis (PCA), partial least squares-discriminant analysis (OPLS-DA)) using SIMCA14, and univariate t-test analysis was performed using SPSS26.0 to screen differential metabolites.

[0098] 7.3 Acquisition and verification of MRM ion pairs

[0099] The process of generating MRM transition sets for quality control samples in positive and negative ion modes of the targeted metabolome is as follows: Figure 3 shown.

[0100] A total of 3564 MS were detected in the NFSWI-DDA data in positive ion mode 1Characteristic peaks, including 1187 MS 1 Characteristic peaks were removed as isotopes or low abundance. 1 Of the characteristic peaks, 1952 generated appropriate MRM transitions in MRM-Ion PairFinder. 1 The characteristic peaks are due to the 2 Lack of MS with peak-to-peak correlation scores greater than 0.8 in the consensus spectrum 2 1952 MRM transitions were manually analyzed and 1633 MS ions were successfully detected in the quality control samples using UPLC-QQQ MRM mode. 1 Characteristic peak (83.66%).

[0101] A total of 3128 MS were detected in negative ion mode 1 Of the characteristic peaks, 1697 generated appropriate MRM transitions in the MRM-Ion Pair Finder, and 1369 MS were successfully detected in the quality control samples after manual analysis. 1 Features (80.67%).

[0102] When verifying the acquired MRM ion pair information, it was found that some ion pairs could not be detected, or the response was very low and could not be completely distinguished from the baseline. Therefore, it was necessary to verify the method for obtaining MRM ion pairs. The extraction verification of ion pairs was conducted using mixed standards. It was found that the extracted Q1 m / z of the standard was consistent with the measured Q1 m / z, △ppm <5ppm, and the extracted Q1t R and the measured Q1 t R Basically the same, most compounds △t R <0.2min, when there is interference with the characteristic peak, △t may appear R >0.2min, indicating that the method for obtaining MRM ion pairs adopted by the present invention is correct and reasonable. It also provides inspiration that when verifying ion pair information, Q1 information should be manually extracted as an auxiliary to determine retention time. In addition, this experiment also verifies the collision energy obtained by ion pairs, and compares the responses of the same ion pair under different SRM collision energies NCE10, 20, 30, 40, and 50V. It is found that the optimal CE value obtained by MRM finder is correct, indicating that the method for obtaining MRM ion pair information adopted is correct and reasonable.

[0103] 7.4 Annotated MRM ion pairs in CSH TM Complementarity of C18 and BEH Amide

[0104] By comparing the 475 annotated ion transitions on the two columns CSH TM C 18The response, peak shape, and peak width on CSH and BEH Amide were used to determine the chromatographic column and positive and negative ion modes expressed by each ion pair during the targeted analysis. TM C 18 The response, peak shape, and peak width of CSH Amide are quite different from those of BEH Amide. TM C 18 The response on the chromatographic column is significantly higher than that on BEH Amide, up to 3 to 7 times, and on CSH TM C 18 The peak shape on the column is better, △t R Smaller, smaller precision RSD; most amino acids in CSH TM C 18 The retention time in the chromatographic column is close, and the separation is poor, while the retention time on the BEH Amide column is dispersed, the peak shape is good, the isomers can be clearly distinguished, and the response is high. Therefore, 217 compounds were analyzed using the BEH Amide column, including 128 in the positive ion mode and 89 in the negative ion mode; CSH TM C 18 The column analyzed 258 compounds, including 134 in positive ion mode and 124 in negative ion mode. The chromatograms are shown in Figure 4 .

[0105] Example 2

[0106] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that during the biological sample preparation process, the 75% v / v methanol aqueous solution is replaced with a 25% v / v methanol aqueous solution.

[0107] Example 3

[0108] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that during the biological sample preparation process, the 75% v / v methanol aqueous solution is replaced with a 50% v / v methanol aqueous solution.

[0109] Example 4

[0110] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that during the biological sample preparation process, the 75% v / v methanol aqueous solution is replaced with methanol.

[0111] Example 5

[0112] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that during the biological sample preparation process, the 75% v / v methanol aqueous solution is replaced with a 50% v / v methanol acetonitrile solution.

[0113] Example 6

[0114] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that during the biological sample preparation process, the 75% v / v methanol aqueous solution is replaced with a 75% v / v methanol acetonitrile solution.

[0115] Example 7

[0116] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that in the biological sample preparation process, 75% v / v methanol aqueous solution is replaced with acetonitrile.

[0117] Example 8

[0118] This example provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that in the HRMS liquid mass spectrometry conditions, the chromatographic column is changed to Luna Omega Polar C 18 (2.1×100mm, 1.6μm).

[0119] Example 9

[0120] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that in the HRMS liquid mass spectrometry conditions, the chromatographic column is changed to CORTECS UPLC T3 (2.1×100 mm, 1.6 μm).

[0121] Example 10

[0122] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that in the HRMS liquid mass spectrometry conditions, the mass spectrometry data acquisition mode is NFSWI-DIA.

[0123] Embodiment 11

[0124] This embodiment provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that in the HRMS liquid mass spectrometry conditions, the mass spectrometry data acquisition mode is DIA.

[0125] Comparative Example 1

[0126] This comparative example provides a high-throughput metabolite detection method. The specific operation of this detection method is basically the same as that of Example 1, except that in the HRMS liquid mass spectrometry conditions, the reverse phase chromatographic column is changed to ACQUITY UPLC HSS T3 (2.1×100 mm, 1.8 μm), CORECS Shield RP 18 (2.1×100mm, 1.6μm), or CORECS UPLC C 18 + (2.1×100 mm, 1.6 μm), the amino column was changed to ACQUITY UPLC BEH C 18 (2.1×100mm, 1.7μm) or ACQUITY UPLCBEH C8 (2.1×100mm, 1.7μm).

[0127] Comparative Example 2

[0128] This comparative example provides a high-throughput metabolite detection method. The specific operation of the detection method is basically the same as that of Example 1, except that in the HRMS liquid mass condition, the data acquisition mode of the mass spectrometer under the reverse phase chromatographic column CSH is changed to conventional data dependent acquisition (DDA), fixed segmented window data dependent acquisition (FSW-DDA), fixed segmented window interval data dependent acquisition (FSWI-DDA) or non-fixed segmented window data dependent acquisition (NFSW-DDA).

[0129] Test Example 1

[0130] This test example compares the response ratio, total response value, maximum value and minimum value of the characteristic peaks extracted from the biological samples of Examples 1 to 7 under the following detection conditions. Figure 5 and Figure 6 shown.

[0131] Chromatographic conditions: U3000 ultra-high performance liquid system was used for detection. The chromatographic column was ACQUITY UPLC HSS T3 (2.1 mm × 100 mm, 1.8 μm), and the mobile phase was 0.1% formic acid aqueous solution (A) and acetonitrile (D) for gradient elution. The elution gradient was: 0-7 min, 3%-100% D; 7-9 min, 100%-100% D; 9-9.1 min, 100%-3% D; 9.1-10 min, 3%-3% D; the flow rate was 0.4 mL / min, the injection volume was 2 μL, and the column oven was set at 35°C.

[0132] Mass spectrometry conditions: Data acquisition was performed using a HRMS mass spectrometer, with a high-energy electrospray ionization source (HESI source) and Full-MS / ddMS in positive and negative ion modes. 2Simultaneous acquisition, ion source parameters: spray voltage of 3.5 kV in positive ion mode, 2.8 kV in negative ion mode, capillary temperature of 320 °C, ion source auxiliary temperature of 350 °C, sheath gas (N2) flow rate of 35 L / h, auxiliary gas (N2) flow rate of 10 L / h, collision energy settings of 15 V, 30 V, 45 V, scanning range of m / z 100-1000, resolution of 60000.

[0133] Depend on Figure 5 It can be seen that when 50% v / v methanol aqueous solution (ie, MEOH / H2O (1:1)) and ACN are used as solvents for protein precipitation, the total response of the extracted characteristic peaks is low, and the proportion of low-response characteristic peaks is high, indicating that the two precipitation solvents are less effective. There is no significant difference in the response proportion and total response value of the other five solvent systems. Among them, when 50% v / v methanol acetonitrile solution is used as the solvent for protein precipitation, the lowest response value of the characteristic peak is higher than that of the other four solvents, and the low-content components are higher than those of other solvent systems. Figure 6 It can be seen that when 50% v / v methanol acetonitrile solution, 50% v / v methanol aqueous solution, 75% v / v methanol acetonitrile solution and 75% v / v methanol aqueous solution are used as solvents for precipitating proteins, according to the comprehensive situation of the extraction characteristic peak response ratio, total response value, maximum value and minimum value, 75% v / v methanol acetonitrile solution and 75% v / v methanol aqueous solution are obviously better as solvents for precipitating proteins.

[0134] Test Example 2

[0135] This test example compares the peak capacity and response value of the Pooled quality control sample using the chromatographic columns of Examples 1, 8, 9 and Comparative Example 1. The information of each chromatographic column is shown in Table 2.

[0136] Table 2 Related information of chromatographic column

[0137]

[0138] The results are as follows Figure 7 As shown, the UPLC CSH used in Examples 1, 8, and 9 TM C 18 , ACQUITY UPLC BEH Amide, Luna Omega Polar C 18 The peak capacity of CORTECS UPLC T3 column is better than that of the other five columns, while that of UPLC CSH TM C 18 The cumulative response values ​​of the characteristic peaks of ACQUITY UPLC BEH Amide were significantly higher than those of other columns.

[0139] Test Example 3

[0140] In this test example, standard samples with different categories (organic acids, amino acids, fatty acids, nucleotides, etc.), mass numbers m / z, TPSA, and XlogP values ​​(see Table 3 for standard sample information) were selected to represent the overall information of endogenous biological compounds. The retention time t of the same standard sample solution in different liquid phase systems was used to determine the overall information of endogenous biological compounds. R The distribution and peak shape are the reference indicators for selecting the mobile phase system. TM C 18 Liquid phase system under chromatographic column conditions.

[0141] Table 3 Standard product information

[0142]

[0143]

[0144] The results are as follows Figure 8 As shown in the figure, in the mobile phase system of acetonitrile-0.1% formic acid water, methanol-0.1% formic acid water, methanol / acetonitrile (1:1)-0.1% formic acid water, and the organic phase was methanol / acetonitrile (1:1, v:v), the t R The dispersion is more dispersed, and the number of compounds that can be accurately quantified is greater (as shown in Figures A and B); comparing the five types of buffer salts and the proportions of addition (see Figure C), there is no significant difference in the number of characteristic peaks among the buffer salt systems A, B, and C (the organic phase is methanol / acetonitrile (1:1, v:v)), and the cumulative response (cumulative peak area of ​​the characteristic peak) and average response of the characteristic peaks of buffer salt system C are significantly higher than those of other buffer salt systems.

[0145] Test Example 4

[0146] This test example uses MS 2 Information abundance, quantitative precision, MS 2 Quality and convenience of data processing were used as evaluation indicators. Based on the segmentation strategy of Q1 non-fixed window acquisition calculated by R software using the Swath Tuner installation package (Table 1), the plasma samples were analyzed using the seven MS in Example 1, Example 10, Example 11 and Comparative Example 2 based on MS-DIAL software and MS Finder software. 2 MS acquired in information acquisition mode 2 The information was compared.

[0147] First, HRMS was used in full scan mode (Full Scan) 1 The abundance TIC of the characteristic peak Q1 of the biological sample was obtained in the acquisition mode, and the segmentation strategy of non-fixed window acquisition was calculated. The results showed that MS 1The characteristic peaks are unevenly distributed over the mass range. When the Q1 segmentation window is fixed, the probability of cross-fragment ion interference within a specific window is higher. The calculated total ion current of the variable window is more uniform than that of the fixed window, which can obtain more accurate MS 2 The information is used for the generation and qualitative analysis of MRM transitions. The optimal segmentation scheme for Q1 is selected when the maximum response of the non-fixed window is less than 1 / 2 of the maximum response of the fixed window and the average response is less than 1 / 3 of the maximum response of the fixed window. In POS mode, Q1 is divided into 10 windows (see Fig. 9 ), NEG mode is divided into 7 windows for optimal (see Fig.10 ).

[0148] MS 2 Metabolic signature detection quantity, MS 2 The number of metabolic feature matches and MS screening with S / N>10 2 Metabolic signature detection quantity, MS 2 The number of metabolic feature matches was used to evaluate the abundance of secondary information obtained. 2 Information evaluation (see Fig.11 ), MS acquired in three modes: NFSWI-DDA, DIA and NFSWI-DIA 2 The feature coverage is significantly higher than that of other modes. 2 The number of characteristic peaks is slightly higher than that of the NFSWI-DDA mode, but the MS 2 Number, and MS with S / N>10 2 The number of valid MSs obtained by NFSWI-DDA and NFSWI-DIA is slightly higher than that of DIA and NFSWI-DIA. 2 There was no significant difference in information coverage.

[0149] The generation of accurate quantitative information is very important for the stability evaluation of metabolomics methods. Technical differences should be minimized during sample preparation and data collection. Under different data collection methods, there are obvious differences in peak abundance, peak shape, and quantitative precision. Among the metabolic features generated in the seven acquisition modes, more than 80% of the characteristic peaks have an RSD of less than 20%, indicating that various data collection methods basically meet the requirements of metabolite precision and can be used for metabolomics analysis. In the NFSWI-DDA mode, the proportion of characteristic peaks with RSD < 20% is higher than that of the other four DDA modes to a certain extent, and is significantly higher than that of the traditional DDA mode. There is no significant difference in quantitative precision between the NFSWI-DDA mode and the NFSWI-DIA mode.

[0150] Afterwards, this experiment compared the two MSs, NFSWI-DDA and NFSWI-DIA. 2 The coverage and precision are higher than those obtained by other modes of MS 2 Dot Product score of the spectrum is used to evaluate MS 2 The quality and reliability of the spectrum. The higher the point score, the better the MS 2 Spectra and MS DIAL and MS Finder databases include MS 2 The higher the spectral matching degree, the higher the MS 2 The higher the quality of the spectrum, the higher the MS score. 2 The spectra are of reference significance for the identification of compounds. The results show that in positive ion mode, the characteristic peaks of NFSWI-DDA and NFSWI-DIA are MS 2 The proportions of the points with a score greater than 60% were 25.8% and 0.90%, respectively. In the negative ion mode, the characteristic peaks of NFSWI-DDA and NFSWI-DIA were MS 2 The percentage of points with a score greater than 60% was 27.47%, indicating that NFSWI-DDA has a significant advantage over NFSWI-DIA in annotation of characteristic peaks. In addition, compared with the complex deconvolution process required in NFSWI-DIA data processing, the data processing steps of NFSWI-DDA mode are significantly simpler.

[0151] Based on the above results, it can be seen that the NFSWI-DDA mode has high coverage, good precision, and MS 2 It has the advantages of high spectral quality, simple spectral processing, and effective and reliable compound annotation, which are superior to other acquisition modes.

[0152] Test Example 5

[0153] This test example conducted a methodological investigation on the method of Example 1: the linearity, precision and repeatability of the 475 annotated compounds were investigated, and the recovery, matrix effect and stability of the 327 compounds compared with the standard were investigated, in order to quantitatively analyze the 327 metabolites.

[0154] 1. Linear investigation

[0155] The nitrogen-purged samples were diluted or concentrated into 10 gradients (1 / 50, 1 / 20, 1 / 10, 1 / 5, 1 / 2, 1, 2, 5, 10, 20) to ensure the same concentration of the isotope internal standard. 4 μL of each sample was injected into the TQ-MS system, and the samples were analyzed from low concentration to high concentration, and repeated three times. The least squares method was used to establish a linear regression equation, and a linear standard curve was drawn with the ratio of the peak area of ​​the target compound to the internal standard in plasma (Y) as the ordinate and the concentration of the target compound as the abscissa (X), and the R of each metabolite was calculated. 2 If R 2 >0.95 metabolites accounted for >60% and R 2 If the proportion of metabolites with a value of >0.8 is >80%, the linearity is considered to meet the standard. Fig.12 Figure A), R 2 87 metabolites with ≥0.999; 0.999>R 2 There are 216 metabolites with a value of ≥0.99; 0.99>R 2 There are 99 metabolite types with a value of ≥0.95; 0.95>R 2 There are 47 metabolites with R ≥0.8; 2 <0.8, among which R 2 Metabolites with R ≥ 0.95 accounted for 84.6%; 2 Metabolites with a value of ≥0.8 accounted for 94.5%, indicating that the quasi-targeted metabolomics method established by the present invention has good linearity. In addition, this experiment used standard solutions to examine the linear standard curves of 327 metabolites compared with standard samples, and found that the detection limits of different categories of compounds were very different. For example, the detection limit of most amino acid compounds was as low as 0.1 ng / mL, while the detection limit of nucleotide compounds was generally high, with the detection line of some nucleotides reaching 50 ng / mL. The quantitative limit range of 327 metabolites was: 0.1 to 50 ng / mL.

[0156] 2. Precision inspection

[0157] The same sample was injected 10 times in one day for three consecutive days, and the CV was calculated to evaluate the intra-day and inter-day precision. When the metabolites with CV values ​​<30% accounted for more than 90% and 80%, respectively, it was considered that the intra-day precision and inter-day precision were met. The results showed that the intra-day precision (see Fig.12 B in Figure ) and inter-day precision (see Fig.12 The data in Figure C show that 96.2% and 96.8% of the metabolites have RSD ≤ 15%, respectively, indicating that the method has good precision.

[0158] 3. Repeatability

[0159] At the same time, 10 QC samples were prepared in parallel and injected three times. The RSD value of the peak area of ​​each metabolite after injection was used to evaluate the repeatability. Fig.12 As shown in Figure D in the figure, 322 metabolites had RSD≤5%; 79 metabolites had 5%<RSD≤10%; 36 metabolites had 10%<RSD≤15%, that is, the metabolites with RSD≤15% accounted for 92.0%, indicating that the established method has good repeatability.

[0160] 4. Recovery rate

[0161] The recovery rate of the quantitative detection method was evaluated by the method of sample addition recovery rate. The recovery rate and matrix effect rationality were evaluated by the standard that the metabolites with 80% ≤ recovery rate or matrix effect ≤ 120% accounted for 80% and RSD ≤ 80% accounted for more than 80%. Fig.13 The analysis of Figure A in Figure 2 showed that there were 96 metabolites with recovery rates ranging from 90% to 110%, and those with recovery rates ranging from 80% to 120% accounted for 93.0% of the total (327 metabolites); there were 121 metabolites with RSD ≤ 5% (accounting for 37.0%), and those with RSD ≤ 15% accounted for 91.7%.

[0162] 5. Matrix Effect

[0163] Among the 327 metabolites that completed the standard comparison, there were 154 metabolites with matrix effects ranging from 90% to 110% (accounting for 47.1%); there were 137 metabolites with matrix effects ranging from 80% to 90% or 110% to 120% (accounting for 41.9%), and there were 305 metabolites with matrix effect RSD ≤ 15% (accounting for 93.6%), see Fig.13 Figure B in .

[0164] 6. Stability

[0165] The freeze-thaw stability and long-term stability of the method were evaluated by the accuracy and CV values ​​after three freeze-thaw cycles and storage at -80°C for 1 month. Fig.13 Figure C in the figure shows that there are 306 metabolites with an accuracy range of 80% to 120% (accounting for 93.3%), and 299 metabolites with RSD ≤ 15% (accounting for 91.4%); after being stored in a -80℃ ultra-low temperature refrigerator for one month, there are 286 metabolites with an accuracy range of 80% to 120% (accounting for 87.5%), and 278 metabolites with RSD ≤ 15% (accounting for 85.0%), see Fig.13 Figure D in .

[0166] The above results show that the quasi-targeted metabolomics method established in the present invention meets the requirements for quantitative detection of biological samples and can be used for quasi-targeted metabolomics analysis of 475 metabolites in biological samples, of which 327 metabolites can be measured.

[0167] Example 12

[0168] In order to evaluate the applicability of the pseudo-targeting method established in Example 1, this example applied it to the pseudo-metabolomics study of LPS-induced liver injury in mice.

[0169] The samples were obtained from plasma and liver of normal mice and mice with LPS-induced liver injury. QC samples were included in the analysis batch, and the stability of the established quasi-targeted method was evaluated to remove metabolites that were not detected in 80% of the samples in each sample group and metabolites with CV values ​​greater than 30% in QC samples. The distribution of the values ​​of the "first component" of QC samples in plasma and liver PCA analysis was Fig.14 These data show that the variation in the QC samples was within 2 std.dev., indicating that the analyzed sequences were stable.

[0170] Based on the data quality assessment, multivariate and univariate analyses were performed. First, the PCA plots of plasma and liver were used to understand the overall differences in metabolic profiles between the normal group and the LPS group. Fig.14 Figure A (plasma) and Figure D (liver) in the figure show that the metabolism of the two groups is clearly distinguished. Then, OPLS-DA was used to establish a supervised classification model and verify whether the model is reliable. The results of 200 permutation tests ( Fig.14 Figures C and F in Figure 3 show R 2 The intercept is greater than zero, Q 2 The intercept is less than zero, indicating that the model is not overfitted and is reliable, with good predictive ability. Afterwards, multivariate statistical analysis and univariate analysis were combined to define the differential metabolites between the groups (compounds with significant changes), with VIP>1, T test P<0.05 and Fold Change value 2 as the standard. The results showed that 45 and 39 differential metabolites of LPS vs Control in plasma and liver were screened, respectively. The 10 most significant metabolites in plasma were serine, alanine, creatine, allantoin, uridine, taurocholic acid, xanthine, choline, palmitoleic acid and ursodeoxycholic acid, and the 10 most significant metabolites in liver were aminoethanesulfonic acid, choline, taurocholic acid, alanine, myristic acid, palmitoleic acid, cholesterol, uridine, chenodeoxycholic acid and citrulline.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-throughput metabolite detection method, characterized in that: Liquid chromatography-mass spectrometry is used to detect metabolites in a biological sample to be tested, and the mass spectrometry is a quadrupole electrostatic field orbital trap high-resolution mass spectrometry and a triple quadrupole mass spectrometry; the mass spectrometry conditions of the high-resolution quadrupole mass spectrometry include: collecting HRMS data in positive and negative ion modes with non-fixed segmented window interval data-dependent acquisition, non-fixed segmented window interval data-independent acquisition, or conventional data-independent acquisition mode, and acquiring and annotating MRM ion pairs; the MRM list is transitioned to a triple quadrupole mass spectrometry, and the mass spectrometry conditions of the triple quadrupole mass spectrometry include: verifying and detecting MRM ion pairs in positive and negative ion modes.

2. The high-throughput metabolite detection method according to claim 1, characterized in that: The high-resolution quadrupole mass spectrometer collects data in a non-fixed segmented window interval data-dependent acquisition mode.

3. The high-throughput metabolite detection method according to claim 1, characterized in that: The biological sample to be tested includes a pre-treated blood sample or a liver sample; The operation of pre-treating the blood sample includes: mixing the blood sample with an internal standard solution and a solvent for precipitating proteins, centrifuging after ultrasonic treatment in an ice-water bath, taking the supernatant, blowing dry with nitrogen, re-dissolving before detection, centrifuging, and taking the supernatant; The operation of pretreating the liver sample includes: homogenizing the liver sample with physiological saline to obtain a homogenate, mixing the homogenate with an internal standard solution and a solvent for precipitating protein, centrifuging after ultrasonic treatment in an ice water bath, taking the supernatant, blowing dry with nitrogen, re-dissolving before detection, centrifuging, and taking the supernatant; the solvent for precipitating protein is 25% to 100% v / v methanol aqueous solution, 50% to 75% v / v methanol acetonitrile solution or acetonitrile, and the solvent for re-dissolving is 25% to 100% v / v methanol aqueous solution, 50% to 75% v / v methanol acetonitrile solution or acetonitrile.

4. The high-throughput metabolite detection method according to claim 3, characterized in that: The solvent for precipitating the protein is a 50% v / v methanol-acetonitrile solution, and the solvent for re-dissolving is a 75% v / v methanol-acetonitrile solution or a 75% v / v methanol aqueous solution.

5. The high-throughput metabolite detection method according to claim 1, characterized in that: When the mass spectrometry is the mass spectrometry condition of the high-resolution quadrupole mass spectrometry, the liquid chromatography-mass spectrometry technology includes liquid chromatography-mass spectrometry under reverse phase chromatography column conditions and liquid chromatography-mass spectrometry under amino chromatography column conditions.

6. The high-throughput metabolite detection method according to claim 5, characterized in that: In the LC-MS under the reverse phase chromatographic column conditions, the chromatographic conditions include: The chromatographic column is CSH C 18 Chromatographic columns; Mobile phase A was acetonitrile-methanol in a volume ratio of 1:1, and mobile phase B was water containing 5 mM ammonium formate, and gradient elution was performed, and the program of the gradient elution was: 0-1 min: 99% B; 1-3 min: 99% B-50% B; 3-8 min: 50% B-1% B; 8-9 min: 1% B; The column temperature was 35-45°C; the flow rate was 0.3 mL / min; The mass spectrometry conditions include: obtaining the abundance of the characteristic peak Q1 of the biological sample in full scan mode, MS 1 The full scan range is 100 to 1000, and the resolution is 120000; data is then collected in NFSWI-DDA or NFSWI-DIA mode.

7. The high-throughput metabolite detection method according to claim 5, characterized in that: In the LC-MS under the amino chromatographic column conditions, the chromatographic conditions include: The chromatographic column is a BEH Amide column; Mobile phase A is acetonitrile-water with a volume ratio of 95:5 and contains 5 mM ammonium formate, and mobile phase B is water-acetonitrile with a volume ratio of 95:5 and contains 5 mM ammonium formate; gradient elution is performed, and the program of the gradient elution is: 0-17 min: 5% B-40% B; 17-20 min: 40% B; 20-23 min: 40% B-5% B; The column temperature was 45°C; the flow rate was 0.2 mL / min; Mass spectrometry conditions include: Full-ddMS 2 Mode data acquisition, Full scan resolution: 60000, ddMS 2 Resolution: 30000; electrospray ion source; auxiliary gas flow rate is 7Arb; sheath gas flow rate is 35Arb; capillary temperature is 320℃; nebulizer gas temperature is 420℃; spray voltage is 3.50kV (POS) or 2.80kV (NEG); collision energy NCE is 15 / 30 / 45V.

8. The high-throughput metabolite detection method according to claim 1, characterized in that: When the mass spectrometer is a triple quadrupole mass spectrometer, the liquid chromatography-mass spectrometry technique includes liquid chromatography-mass spectrometry under reverse phase chromatography column conditions and liquid chromatography-mass spectrometry under amino chromatography column conditions.

9. The high-throughput metabolite detection method according to claim 8, characterized in that: In the LC-MS under the reverse phase chromatographic column conditions, the chromatographic conditions include: The chromatographic column is CSH C 18 Chromatographic columns; Mobile phase A was acetonitrile-methanol in a volume ratio of 1:1, and mobile phase B was water containing 5 mM ammonium formate, and gradient elution was performed, and the program of the gradient elution was: 0-4 min: 99% B-97% B; 4-7 min: 97% B-80% B; 7-16 min: 80% B-0% B; 16-18 min: 0% B; The column temperature was 40-50°C; the flow rate was 0.5 mL / min; The mass spectrometry conditions include: positive ion and negative ion mode detection, respectively; capillary voltage: +3.5kV / -2.8kV; cone voltage: 30V; capillary temperature: 500°C; desolvation temperature: 450°C; desolvation gas flow rate of 1000L / Hr; 1000L / Hr; auxiliary gas: 50L / Hr, and the detection mode adopts MRM mode.

10. The high-throughput metabolite detection method according to claim 8, characterized in that: In the LC-MS under the amino chromatographic column conditions, the chromatographic conditions include: The chromatographic column is a BEH Amide column; Mobile phase A is acetonitrile-water with a volume ratio of 95:5 and contains 5 mM ammonium formate, and mobile phase B is water-acetonitrile with a volume ratio of 95:5 and contains 5 mM ammonium formate; gradient elution is performed, and the program of the gradient elution is: 0-17 min: 5% B-40% B; 17-20 min: 40% B; 20-23 min: 40% B-5% B; The column temperature was 45°C; the flow rate was 0.2 mL / min; The mass spectrometry conditions include: positive ion and negative ion mode detection, respectively; capillary voltage: +3.5kV / -2.8kV; cone voltage: 30V; capillary temperature: 500°C; desolvation temperature: 450°C; desolvation gas flow rate of 1000L / Hr; 1000L / Hr; auxiliary gas: 50L / Hr, and the detection mode adopts MRM mode.

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